Welcome to Tetragon documentation

• 1: Overview
• 2: Getting Started
• 2.1: Quick Kubernetes Install
• 2.2: Quick Local Docker Install
• 2.3: Execution Monitoring
• 2.4: File Access Monitoring
• 2.5: Network Monitoring
• 2.6: Policy Enforcement
• 3: Installation and Configuration
• 3.1: Deploy on Kubernetes
• 3.2: Deploy as a container
• 3.3: Deploy with a package
• 3.4: Configure Runtime Hooks
• 3.5: Install tetra CLI
• 3.6: Verify installation
• 3.7: Configure Tetragon
• 3.8: Metrics
• 3.9: FAQ
• 4: Concepts
• 4.1: Events
• 4.2: Tracing Policy
• 4.2.1: Example
• 4.2.2: Hook points
• 4.2.3: Options
• 4.2.4: Selectors
• 4.2.5: Tags
• 4.2.6: Kubernetes Identity Aware Policies
• 4.3: Runtime Hooks
• 4.4: Enforcement
• 4.4.1: Persistent enforcement
• 4.5: Event throttling
• 5: Policy Library
• 5.1: Tetragon Observability Policies
• 6: Use Cases
• 6.1: Process lifecycle
• 6.1.1: Process execution
• 6.1.2: Advanced Process execution
• 6.1.3: Privileged execution
• 6.2: Filename access
• 6.3: Network observability
• 6.4: Linux process credentials
• 6.4.1: Monitor Process Credentials changes at the System Call layer
• 6.4.2: Monitor Process Credentials changes at the Kernel layer
• 6.5: Host System Changes
• 6.5.1: Monitor Linux Kernel Modules
• 6.6: Security Profiles
• 6.6.1: Record Linux Capabilities Usage
• 7: Contribution Guide
• 7.1: Development setup
• 7.2: Making changes
• 7.3: Running tests
• 7.4: Documentation
• 7.5: Submitting a pull request
• 7.6: Developer's certificate of origin
• 7.7: Release & upgrade notes
• 8: Reference
• 8.1: Daemon Configuration
• 8.2: Helm chart
• 8.3: gRPC API
• 8.4: Metrics
• 9: Troubleshooting
• 10: Resources

Getting Started with Tetragon

Quickly get started and learn how to install, deploy and configure it

Getting started

1 - Overview

Cilium Tetragon component enables powerful realtime, eBPF-based Security Observability and Runtime Enforcement.

Tetragon detects and is able to react to security-significant events, such as

• Process execution events
• System call activity
• I/O activity including network & file access

When used in a Kubernetes environment, Tetragon is Kubernetes-aware - that is, it understands Kubernetes identities such as namespaces, pods and so-on - so that security event detection can be configured in relation to individual workloads.

Functionality Overview

eBPF Real-Time

Tetragon is a runtime security enforcement and observability tool. What this means is Tetragon applies policy and filtering directly in eBPF in the kernel. It performs the filtering, blocking, and reacting to events directly in the kernel instead of sending events to a user space agent.

For an observability use case, applying filters directly in the kernel drastically reduces observation overhead. By avoiding expensive context switching and wake-ups, especially for high frequency events, such as send, read, or write operations, eBPF reduces required resources. Instead, Tetragon provides rich filters (file, socket, binary names, namespace/capabilities, etc.) in eBPF, which allows users to specify the important and relevant events in their specific context, and pass only those to the user-space agent.

eBPF Flexibility

Tetragon can hook into any function in the Linux kernel and filter on its arguments, return value, associated metadata that Tetragon collects about processes (e.g., executable names), files, and other properties. By writing tracing policies users can solve various security and observability use cases. We provide a number of examples for these in the repository and highlight some below in the ‘Getting Started Guide’, but users are encouraged to create new policies that match their use cases. The examples are just that, jumping off points that users can then use to create new and specific policy deployments even potentially tracing kernel functions we did not consider. None of the specifics about which functions are traced and what filters are applied are hard-coded in the engine itself.

Critically, Tetragon allows hooking deep in the kernel where data structures can not be manipulated by user space applications avoiding common issues with syscall tracing where data is incorrectly read, maliciously altered by attackers, or missing due to page faults and other user/kernel boundary errors.

Many of the Tetragon developers are also kernel developers. By leveraging this knowledge base Tetragon has created a set of tracing policies that can solve many common observability and security use cases.

eBPF Kernel Aware

Tetragon, through eBPF, has access to the Linux kernel state. Tetragon can then join this kernel state with Kubernetes awareness or user policy to create rules enforced by the kernel in real time. This allows annotating and enforcing process namespace and capabilities, sockets to processes, process file descriptor to filenames and so on. For example, when an application changes its privileges we can create a policy to trigger an alert or even kill the process before it has a chance to complete the syscall and potentially run additional syscalls.

What’s next?

• Getting Started Get started with Tetragon.
• Concepts: Learn about Tetragon events.

2 - Getting Started

2.1 - Quick Kubernetes Install

Create a cluster

If you don’t have a Kubernetes Cluster yet, you can use the instructions below to create a Kubernetes cluster locally or using a managed Kubernetes service:

• GKE
• AKS
• EKS
• Kind

export NAME="$(whoami)-$RANDOM"
export ZONE="us-west2-a"
gcloud container clusters create "${NAME}" --zone ${ZONE} --num-nodes=1
gcloud container clusters get-credentials "${NAME}" --zone ${ZONE}

export NAME="$(whoami)-$RANDOM"
export AZURE_RESOURCE_GROUP="${NAME}-group"
az group create --name "${AZURE_RESOURCE_GROUP}" -l westus2
az aks create --resource-group "${AZURE_RESOURCE_GROUP}" --name "${NAME}"
az aks get-credentials --resource-group "${AZURE_RESOURCE_GROUP}" --name "${NAME}"

export NAME="$(whoami)-$RANDOM"
eksctl create cluster --name "${NAME}"

cat <<EOF > kind-config.yaml
apiVersion: kind.x-k8s.io/v1alpha4
kind: Cluster
nodes:
  - role: control-plane
    extraMounts:
      - hostPath: /proc
        containerPath: /procHost
EOF
kind create cluster --config kind-config.yaml
EXTRA_HELM_FLAGS=(--set tetragon.hostProcPath=/procHost) # flags for helm install

The commands in this Getting Started guide assume you use a single-node Kubernetes cluster. If you use a cluster with multiple nodes, be aware that some of the commands shown need to be modified. We call out these changes where they are necessary.

Deploy Tetragon

To install and deploy Tetragon, run the following commands:

helm repo add cilium https://helm.cilium.io
helm repo update
helm install tetragon ${EXTRA_HELM_FLAGS[@]} cilium/tetragon -n kube-system
kubectl rollout status -n kube-system ds/tetragon -w

By default, Tetragon will filter kube-system events to reduce noise in the event logs. See concepts and advanced configuration to configure these parameters.

Deploy demo application

To explore Tetragon it is helpful to have a sample workload. Here we use Cilium’s demo application, but any workload would work equally well:

kubectl create -f https://raw.githubusercontent.com/cilium/cilium/v1.15.3/examples/minikube/http-sw-app.yaml

Before going forward, verify that all pods are up and running - it might take several seconds for some pods to satisfy all the dependencies:

kubectl get pods

The output should be similar to this:

NAME                         READY   STATUS    RESTARTS   AGE
deathstar-6c94dcc57b-7pr8c   1/1     Running   0          10s
deathstar-6c94dcc57b-px2vw   1/1     Running   0          10s
tiefighter                   1/1     Running   0          10s
xwing                        1/1     Running   0          10s

What’s Next

Check for execution events.

2.2 - Quick Local Docker Install

Start Tetragon

The easiest way to start experimenting with Tetragon is to run it via Docker using the released container images.

docker run -d --name tetragon --rm --pull always \
    --pid=host --cgroupns=host --privileged             \
    -v /sys/kernel/btf/vmlinux:/var/lib/tetragon/btf    \
    quay.io/cilium/tetragon:v1.2.0

This will start Tetragon in a privileged container running in the background. Running Tetragon as a privileged container is required to load and attach BPF programs. See the Installation and Configuration section for more details.

Run demo application

To explore Tetragon it is helpful to have a sample workload. You can use Cilium’s demo application with this Docker Compose file, but any workload would work equally well:

curl -LO https://github.com/cilium/tetragon/raw/main/examples/quickstart/docker-compose.yaml
docker compose -f docker-compose.yaml up -d

You can use docker container ls to verify that the containers are up and running. It might take a short amount of time for the container images to download.

CONTAINER ID   IMAGE                             COMMAND                  CREATED          STATUS          PORTS                                   NAMES
cace79752d94   quay.io/cilium/json-mock:v1.3.8   "bash /run.sh"           34 seconds ago   Up 33 seconds                                           starwars-xwing-1
4f8422b43b5b   quay.io/cilium/json-mock:v1.3.8   "bash /run.sh"           34 seconds ago   Up 33 seconds                                           starwars-tiefighter-1
7b60618ca8bd   quay.io/cilium/starwars:v2.1      "/starwars-docker --…"   34 seconds ago   Up 33 seconds   0.0.0.0:8080->80/tcp, :::8080->80/tcp   starwars-deathstar-1

What’s next

Check for execution events.

2.3 - Execution Monitoring

At the core of Tetragon is the tracking of all executions in a Kubernetes cluster, virtual machines, and bare metal systems. This creates the foundation that allows Tetragon to attribute all system behavior back to a specific binary and its associated metadata (container, Pod, Node, and cluster).

Observe Tetragon execution events

Tetragon exposes the execution events over JSON logs and GRPC stream. The user can then observe all executions in the system.

Use the following instructions to observe execution events. These instructions assume you have deployed the Cilium demo application in your environment.

• Kubernetes (single node)
• Kubernetes (multiple nodes)
• Docker

kubectl exec -ti -n kube-system ds/tetragon -c tetragon -- tetra getevents -o compact --pods xwing

POD=$(kubectl -n kube-system get pods -l 'app.kubernetes.io/name=tetragon' -o name --field-selector spec.nodeName=$(kubectl get pod xwing -o jsonpath='{.spec.nodeName}'))

kubectl exec -ti -n kube-system $POD -c tetragon -- tetra getevents -o compact --pods xwing

docker exec tetragon tetra getevents -o compact

The tetra get-events -o compact command returns a compact form of the execution events. To trigger an execution event, you will run a curl command inside the “xwing” Pod/container.

• Kubernetes (single node)
• Kubernetes (multiple nodes)
• Docker

kubectl exec -ti xwing -- bash -c 'curl https://ebpf.io/applications/#tetragon'

kubectl exec -ti xwing -- bash -c 'curl https://ebpf.io/applications/#tetragon'

docker exec -ti starwars-xwing-1 curl https://ebpf.io/applications/#tetragon

The CLI will print a compact form of the event to the terminal similar to the following output. The example output below is from Kubernetes; the Docker output is very similar.

🚀 process default/xwing /bin/bash -c "curl https://ebpf.io/applications/#tetragon"
🚀 process default/xwing /usr/bin/curl https://ebpf.io/applications/#tetragon
💥 exit    default/xwing /usr/bin/curl https://ebpf.io/applications/#tetragon 60

The compact execution event contains the event type, the pod name, the binary and the args. The exit event will include the return code; in the case of the curl command above, the return code was 60.

For the complete execution event in JSON format remove the -o compact option from the tetra getevents command.

• Kubernetes (single node)
• Kubernetes (multiple nodes)
• Docker

kubectl exec -ti -n kube-system ds/tetragon -c tetragon -- tetra getevents --pods xwing

kubectl exec -ti -n kube-system $POD -c tetragon -- tetra getevents --pods xwing

docker exec -ti tetragon tetra getevents

The complete execution event includes a lot more details related to the binary and event. See below for a full example of the execution event generated by the curl command used above. In a Kubernetes environment this will include the Kubernetes metadata include the Pod, Container, Namespaces, and Labels, among other useful metadata.

{
  "process_exec": {
    "process": {
      "exec_id": "Z2tlLWpvaG4tNjMyLWRlZmF1bHQtcG9vbC03MDQxY2FjMC05czk1OjEzNTQ4Njc0MzIxMzczOjUyNjk5",
      "pid": 52699,
      "uid": 0,
      "cwd": "/",
      "binary": "/usr/bin/curl",
      "arguments": "https://ebpf.io/applications/#tetragon",
      "flags": "execve rootcwd",
      "start_time": "2023-10-06T22:03:57.700327580Z",
      "auid": 4294967295,
      "pod": {
        "namespace": "default",
        "name": "xwing",
        "container": {
          "id": "containerd://551e161c47d8ff0eb665438a7bcd5b4e3ef5a297282b40a92b7c77d6bd168eb3",
          "name": "spaceship",
          "image": {
            "id": "docker.io/tgraf/netperf@sha256:8e86f744bfea165fd4ce68caa05abc96500f40130b857773186401926af7e9e6",
            "name": "docker.io/tgraf/netperf:latest"
          },
          "start_time": "2023-10-06T21:52:41Z",
          "pid": 49
        },
        "pod_labels": {
          "app.kubernetes.io/name": "xwing",
          "class": "xwing",
          "org": "alliance"
        },
        "workload": "xwing"
      },
      "docker": "551e161c47d8ff0eb665438a7bcd5b4",
      "parent_exec_id": "Z2tlLWpvaG4tNjMyLWRlZmF1bHQtcG9vbC03MDQxY2FjMC05czk1OjEzNTQ4NjcwODgzMjk5OjUyNjk5",
      "tid": 52699
    },
    "parent": {
      "exec_id": "Z2tlLWpvaG4tNjMyLWRlZmF1bHQtcG9vbC03MDQxY2FjMC05czk1OjEzNTQ4NjcwODgzMjk5OjUyNjk5",
      "pid": 52699,
      "uid": 0,
      "cwd": "/",
      "binary": "/bin/bash",
      "arguments": "-c \"curl https://ebpf.io/applications/#tetragon\"",
      "flags": "execve rootcwd clone",
      "start_time": "2023-10-06T22:03:57.696889812Z",
      "auid": 4294967295,
      "pod": {
        "namespace": "default",
        "name": "xwing",
        "container": {
          "id": "containerd://551e161c47d8ff0eb665438a7bcd5b4e3ef5a297282b40a92b7c77d6bd168eb3",
          "name": "spaceship",
          "image": {
            "id": "docker.io/tgraf/netperf@sha256:8e86f744bfea165fd4ce68caa05abc96500f40130b857773186401926af7e9e6",
            "name": "docker.io/tgraf/netperf:latest"
          },
          "start_time": "2023-10-06T21:52:41Z",
          "pid": 49
        },
        "pod_labels": {
          "app.kubernetes.io/name": "xwing",
          "class": "xwing",
          "org": "alliance"
        },
        "workload": "xwing"
      },
      "docker": "551e161c47d8ff0eb665438a7bcd5b4",
      "parent_exec_id": "Z2tlLWpvaG4tNjMyLWRlZmF1bHQtcG9vbC03MDQxY2FjMC05czk1OjEzNTQ4NjQ1MjQ1ODM5OjUyNjg5",
      "tid": 52699
    }
  },
  "node_name": "gke-john-632-default-pool-7041cac0-9s95",
  "time": "2023-10-06T22:03:57.700326678Z"
}

What’s next

Execution events are the most basic event Tetragon can produce. To see how to use tracing policies to enable file monitoring see the File Access Monitoring section of the Getting Started guide. To see a network policy check the Networking Monitoring section.

2.4 - File Access Monitoring

Tracing policies can be added to Tetragon through YAML configuration files that extend Tetragon’s base execution tracing capabilities. These policies perform filtering in kernel to ensure only interesting events are published to userspace from the BPF programs running in kernel. This ensures overhead remains low even on busy systems.

The instructions below extend the example from Execution Monitoring with a policy to monitor sensitive files in Linux. The policy used is file_monitoring.yaml, which you can review and extend as needed. Files monitored here serve as a good base set of files.

Apply the tracing policy

To apply the policy in Kubernetes, use kubectl. In Kubernetes, the policy references a Custom Resource Definition (CRD) installed by Tetragon. Docker uses the same YAML configuration file as Kubernetes, but this file is loaded from disk when the Docker container is launched.

Note that these instructions assume you’ve installed the demo application, as outlined in either the Quick Kubernetes Install or the Quick Docker Install section.

• Kubernetes (single node)
• Kubernetes (multiple nodes)
• Docker

kubectl apply -f https://raw.githubusercontent.com/cilium/tetragon/main/examples/quickstart/file_monitoring.yaml

kubectl apply -f https://raw.githubusercontent.com/cilium/tetragon/main/examples/quickstart/file_monitoring.yaml

wget https://raw.githubusercontent.com/cilium/tetragon/main/examples/quickstart/file_monitoring.yaml
docker stop tetragon
docker run -d --name tetragon --rm --pull always \
  --pid=host --cgroupns=host --privileged \
  -v ${PWD}/file_monitoring.yaml:/etc/tetragon/tetragon.tp.d/file_monitoring.yaml \
  -v /sys/kernel/btf/vmlinux:/var/lib/tetragon/btf \
  quay.io/cilium/tetragon:v1.2.0

Observe Tetragon file access events

With the tracing policy applied you can attach tetra to observe events again:

• Kubernetes (single node)
• Kubernetes (multiple nodes)
• Docker

kubectl exec -ti -n kube-system ds/tetragon -c tetragon -- tetra getevents -o compact --pods xwing

POD=$(kubectl -n kubesystem get pods -l 'app.kubernetes.io/name=tetragon' -o name --field-selector spec.nodeName=$(kubectl get pod xwing -o jsonpath='{.spec.nodeName}'))
kubectl exec -ti -n kube-system $POD -c tetragon -- tetra getevents -o compact --pods xwing

docker exec -ti tetragon tetra getevents -o compact

To generate an event, try to read a sensitive file referenced in the policy.

• Kubernetes (single node)
• Kubernetes (multiple nodes)
• Docker

kubectl exec -ti xwing -- bash -c 'cat /etc/shadow'

kubectl exec -ti xwing -- bash -c 'cat /etc/shadow'

cat /etc/shadow

This will generate a read event (Docker events will omit Kubernetes metadata shown below) that looks something like this:

🚀 process default/xwing /bin/bash -c "cat /etc/shadow"
🚀 process default/xwing /bin/cat /etc/shadow
📚 read    default/xwing /bin/cat /etc/shadow
💥 exit    default/xwing /bin/cat /etc/shadow 0

Per the tracing policy, Tetragon generates write events in responses to attempts to write in sensitive directories (for example, attempting to write in the /etc directory).

• Kubernetes (single node)
• Kubernetes (multiple nodes)
• Docker

kubectl exec -ti xwing -- bash -c 'echo foo >> /etc/bar'

kubectl exec -ti xwing -- bash -c 'echo foo >> /etc/bar'

echo foo >> /etc/bar

In response, you will see output similar to the following (Docker events do not include the Kubernetes metadata shown here).

🚀 process default/xwing /bin/bash -c "echo foo >>  /etc/bar"
📝 write   default/xwing /bin/bash /etc/bar
📝 write   default/xwing /bin/bash /etc/bar
💥 exit    default/xwing /bin/bash -c "echo foo >>  /etc/bar

What’s next

To explore tracing policies for networking see the Networking Monitoring section of the Getting Started guide. To dive into the details of policies and events please see the Concepts section of the documentation.

2.5 - Network Monitoring

In addition to file access monitoring, Tetragon’s tracing policies also support monitoring network access. In this section, you will see how to monitor network traffic to “external” destinations (destinations that are outside the Kubernetes cluster or external to the Docker host where Tetragon is running). These instructions assume you already have Tetragon running in either Kubernetes or Docker, and that you have deployed the Cilium demo application.

Monitoring Kubernetes network access

First, you’ll need to find the pod CIDR and service CIDR in use. In many cases the pod CIDR is relatively easy to find.

export PODCIDR=`kubectl get nodes -o jsonpath='{.items[*].spec.podCIDR}'`

You can fetch the service CIDR from the cluster in some environments. When working with managed Kubernetes offerings (AKS, EKS, or GKE) you will need the environment variables used when you created the cluster.

• GKE
• Kind
• EKS
• AKS

export SERVICECIDR=$(gcloud container clusters describe ${NAME} --zone ${ZONE} | awk '/servicesIpv4CidrBlock/ { print $2; }')

export SERVICECIDR=$(kubectl describe pod -n kube-system kube-apiserver-kind-control-plane | awk -F= '/--service-cluster-ip-range/ {print $2; }')

export SERVICECIDR=$(aws eks describe-cluster --name ${NAME} | jq -r '.cluster.kubernetesNetworkConfig.serviceIpv4Cidr')

export SERVICECIDR=$(az aks show --name ${NAME} --resource-group ${AZURE_RESOURCE_GROUP} | jq -r '.networkProfile.serviceCidr)

Once you have this information, you can customize a policy to exclude network traffic to the networks stored in the PODCIDR and SERVICECIDR environment variables. Use envsubst to do this, and then apply the policy to your Kubernetes cluster with kubectl apply:

wget https://raw.githubusercontent.com/cilium/tetragon/main/examples/quickstart/network_egress_cluster.yaml
envsubst < network_egress_cluster.yaml | kubectl apply -f -

Once the tracing policy is applied, you can attach tetra to observe events again:

• Kubernetes (single node)
• Kubernetes (multiple nodes

kubectl exec -ti -n kube-system ds/tetragon -c tetragon -- tetra getevents -o compact --pods xwing --processes curl

POD=$(kubectl -n kubesystem get pods -l 'app.kubernetes.io/name=tetragon' -o name --field-selector spec.nodeName=$(kubectl get pod xwing -o jsonpath='{.spec.nodeName}'))
kubectl exec -ti -n kube-system $POD -c tetragon -- tetra getevents -o compact --pods xwing --processes curl

Then execute a curl command in the “xwing” Pod to access one of our favorite sites.

 kubectl exec -ti xwing -- bash -c 'curl https://ebpf.io/applications/#tetragon'

You will observe a connect event being reported in the output of the tetra getevents command:

🚀 process default/xwing /usr/bin/curl https://ebpf.io/applications/#tetragon
🔌 connect default/xwing /usr/bin/curl tcp 10.32.0.19:33978 -> 104.198.14.52:443
💥 exit    default/xwing /usr/bin/curl https://ebpf.io/applications/#tetragon 60

You can confirm in-kernel BPF filters are not producing events for in-cluster traffic by issuing a curl to one of our services and noting there is no connect event.

kubectl exec -ti xwing -- bash -c 'curl -s -XPOST deathstar.default.svc.cluster.local/v1/request-landing'

The output should be similar to:

Ship landed

And as expected no new events:

🚀 process default/xwing /usr/bin/curl https://ebpf.io/applications/#tetragon
🔌 connect default/xwing /usr/bin/curl tcp 10.32.0.19:33978 -> 104.198.14.52:443
💥 exit    default/xwing /usr/bin/curl https://ebpf.io/applications/#tetragon 60

Monitoring Docker or bare metal network access

This example also works easily for local Docker users. However, since Docker does not have pod CIDR or service CIDR constructs, you will construct a tracing policy that filters 127.0.0.1 from the Tetragon event log.

First, set the necessary environment variables to the loopback IP address.

export PODCIDR="127.0.0.1/32"
export SERVICECIDR="127.0.0.1/32"

Next, customize the policy using envsubst.

wget https://raw.githubusercontent.com/cilium/tetragon/main/examples/quickstart/network_egress_cluster.yaml
envsubst < network_egress_cluster.yaml > network_egress_cluster_subst.yaml

Finally, start Tetragon with the new policy.

docker stop tetragon
docker run -d --name tetragon --rm --pull always \
  --pid=host --cgroupns=host --privileged               \
  -v ${PWD}/network_egress_cluster_subst.yaml:/etc/tetragon/tetragon.tp.d/network_egress_cluster_subst.yaml \
  -v /sys/kernel/btf/vmlinux:/var/lib/tetragon/btf      \
  quay.io/cilium/tetragon:v1.2.0

Once Tetragon is running, use docker exec to run the tetra getevents command and log the output to your terminal.

docker exec -ti tetragon tetra getevents -o compact

Now remote TCP connections will be logged, but connections to the localhost address are filtered out by Tetragon. You can see this by executing a curl command to generate a remote TCP connect.

curl https://ebpf.io/applications/#tetragon

This produces the following output:

🚀 process  /usr/bin/curl https://ebpf.io/applications/#tetragon
🔌 connect  /usr/bin/curl tcp 192.168.1.190:36124 -> 104.198.14.52:443
💥 exit     /usr/bin/curl https://ebpf.io/applications/#tetragon 0

What’s next

So far you have installed Tetragon and used a couple policies to monitor sensitive files and provide network auditing for connections outside your own cluster and node. Both these cases highlight the value of in-kernel filtering. Another benefit of in-kernel filtering is you can add enforcement to the policies to not only alert via a log entry, but to block the operation in kernel and/or kill the application attempting the operation.

To learn more about policies and events Tetragon can implement review the Concepts section.

2.6 - Policy Enforcement

Tetragon’s tracing policies support monitoring kernel functions to report events, such as file access events or network connection events, as well as enforcing restrictions on those same kernel functions. Using in-kernel filtering in Tetragon provides a key performance improvement by limiting events from kernel to user space. In-kernel filtering also enables Tetragon to enforce policy restrictions at the kernel level. For example, by issuing a SIGKILL to a process when a policy violation is detected, the process will not continue to run. If the policy enforcement is triggered through a syscall this means the application will not return from the syscall and will be terminated.

In this section, you will add network and file policy enforcement on top of the Tetragon functionality (execution, file tracing, and network tracing policy) you’ve already deployed in this Getting Started guide. Specifically, you will:

• Apply a policy that restricts network traffic egressing a Kubernetes cluster
• Apply a block write and read operations to sensitive files

For specific implementation details refer to the Enforcement concept section.

Restricting network traffic on Kubernetes

In this use case you will use a Tetragon tracing policy to block TCP connections outside the Kubernetes cluster where Tetragon is running. The Tetragon policy is namespaced, limiting the scope of the enforcement policy to just the “default” namespace where you installed the demo application in the Quick Kubernetes Install section.

The policy you will use is very similar to the policy you used in the Network Monitoring section, but with enforcement enabled. Although this policy does not use them, Tetragon tracing policies support including Kubernetes filters, such as namespaces and labels, so you can limit a policy to targeted namespaces and Pods. This is critical for effective policy segmentation.

First, ensure you have the proper Pod CIDR captured for use later:

export PODCIDR=`kubectl get nodes -o jsonpath='{.items[*].spec.podCIDR}'`

You will also need to capture the service CIDR for use in customizing the policy. When working with managed Kubernetes offerings (AKS, EKS, or GKE) you will need the environment variables used when you created the cluster.

• GKE
• Kind
• EKS
• AKS

export SERVICECIDR=$(gcloud container clusters describe ${NAME} --zone ${ZONE} | awk '/servicesIpv4CidrBlock/ { print $2; }')

export SERVICECIDR=$(kubectl describe pod -n kube-system kube-apiserver-kind-control-plane | awk -F= '/--service-cluster-ip-range/ {print $2; }')

export SERVICECIDR=$(aws eks describe-cluster --name ${NAME} | jq -r '.cluster.kubernetesNetworkConfig.serviceIpv4Cidr')

export SERVICECIDR=$(az aks show --name ${NAME} --resource-group ${AZURE_RESOURCE_GROUP} | jq -r '.networkProfile.serviceCidr)

When you have captured the Pod CIDR and Service CIDR, then you can customize and apply the enforcement policy. (If you installed the demo application in a different namespace than the default namespace, adjust the kubectl apply command accordingly.)

wget https://raw.githubusercontent.com/cilium/tetragon/main/examples/quickstart/network_egress_cluster_enforce.yaml
envsubst < network_egress_cluster_enforce.yaml | kubectl apply -n default -f -

With the enforcement policy applied, run the tetra getevents command to observe events.

• Kubernetes (single node)
• Kubernetes (multiple nodes)

kubectl exec -ti -n kube-system ds/tetragon -c tetragon -- tetra getevents -o compact --pods xwing

POD=$(kubectl -n kubesystem get pods -l 'app.kubernetes.io/name=tetragon' -o name --field-selector spec.nodeName=$(kubectl get pod xwing -o jsonpath='{.spec.nodeName}'))
kubectl exec -ti -n kube-system $POD -c tetragon -- tetra getevents -o compact --pods xwing

To generate an event that Tetragon will report, use curl to connect to a site outside the Kubernetes cluster:

kubectl exec -ti xwing -- bash -c 'curl https://ebpf.io/applications/#tetragon'

The command returns an error code because the egress TCP connects are blocked. The tetra CLI will print the curl and annotate that the process that was issued a SIGKILL.

command terminated with exit code 137

Making network connections to destinations inside the cluster will work as expected:

kubectl exec -ti xwing -- bash -c 'curl -s -XPOST deathstar.default.svc.cluster.local/v1/request-landing'

The successful internal connection is filtered and will not be shown. The tetra getevents output from the two curl commands should look something like this:

🚀 process default/xwing /bin/bash -c "curl https://ebpf.io/applications/#tetragon"
🚀 process default/xwing /usr/bin/curl https://ebpf.io/applications/#tetragon
🔌 connect default/xwing /usr/bin/curl tcp 10.32.0.28:45200 -> 104.198.14.52:443
💥 exit    default/xwing /usr/bin/curl https://ebpf.io/applications/#tetragon SIGKILL
🚀 process default/xwing /bin/bash -c "curl -s -XPOST deathstar.default.svc.cluster.local/v1/request-landing"
🚀 process default/xwing /usr/bin/curl -s -XPOST deathstar.default.svc.cluster.local/v1/request-landing

Enforce file access restrictions

The following extends the example from File Access Monitoring with enforcement to ensure sensitive files are not read. The policy used is the file_monitoring_enforce.yaml, which you can review and extend as needed. The only difference between the observation policy and the enforce policy is the addition of an action block to SIGKILL the application and return an error on the operation.

To apply the policy:

• Kubernetes (single node)
• Kubernetes (multiple nodes)
• Docker

kubectl delete -f https://raw.githubusercontent.com/cilium/tetragon/main/examples/quickstart/file_monitoring.yaml
kubectl apply -f https://raw.githubusercontent.com/cilium/tetragon/main/examples/quickstart/file_monitoring_enforce.yaml

kubectl delete -f https://raw.githubusercontent.com/cilium/tetragon/main/examples/quickstart/file_monitoring.yaml
kubectl apply -f https://raw.githubusercontent.com/cilium/tetragon/main/examples/quickstart/file_monitoring_enforce.yaml

wget https://raw.githubusercontent.com/cilium/tetragon/main/examples/quickstart/file_monitoring_enforce.yaml
docker stop tetragon
docker run --name tetragon --rm --pull always \
  --pid=host --cgroupns=host --privileged               \
  -v ${PWD}/file_monitoring_enforce.yaml:/etc/tetragon/tetragon.tp.d/file_monitoring_enforce.yaml \
  -v /sys/kernel/btf/vmlinux:/var/lib/tetragon/btf      \
  quay.io/cilium/tetragon:v1.2.0

With the policy applied, you can run tetra getevents to have Tetragon start outputting events to the terminal.

• Kubernetes (single node)
• Kubernetes (multiple nodes)
• Docker

kubectl exec -ti -n kube-system ds/tetragon -c tetragon -- tetra getevents -o compact --pods xwing

POD=$(kubectl -n kubesystem get pods -l 'app.kubernetes.io/name=tetragon' -o name --field-selector spec.nodeName=$(kubectl get pod xwing -o jsonpath='{.spec.nodeName}'))
kubectl exec -ti -n kube-system $POD -c tetragon -- tetra getevents -o compact --pods xwing

docker exec -ti tetragon tetra getevents -o compact

Next, attempt to read a sensitive file (one of the files included in the defined policy):

• Kubernetes (single node)
• Kubernetes (multiple nodes)
• Docker

kubectl exec -ti xwing -- bash -c 'cat /etc/shadow'

kubectl exec -ti xwing -- bash -c 'cat /etc/shadow'

cat /etc/shadow

Because the file is included in the policy, the command will fail with an error code.

kubectl exec -ti xwing -- bash -c 'cat /etc/shadow'

The output should be similar to:

command terminated with exit code 137

This will generate a read event (Docker events will not contain the Kubernetes metadata shown here).

🚀 process default/xwing /bin/bash -c "cat /etc/shadow"
🚀 process default/xwing /bin/cat /etc/shadow
📚 read    default/xwing /bin/cat /etc/shadow
📚 read    default/xwing /bin/cat /etc/shadow
📚 read    default/xwing /bin/cat /etc/shadow
💥 exit    default/xwing /bin/cat /etc/shadow SIGKILL

Attempts to read or write to files that are not part of the enforced file policy are not impacted.

🚀 process default/xwing /bin/bash -c "echo foo >> bar; cat bar"
🚀 process default/xwing /bin/cat bar
💥 exit    default/xwing /bin/cat bar 0
💥 exit    default/xwing /bin/bash -c "echo foo >> bar; cat bar" 0

What’s next

The completes the Getting Started guide. At this point you should be able to observe execution traces in a Kubernetes cluster and extend the base deployment of Tetragon with policies to observe and enforce different aspects of a Kubernetes system.

The rest of the docs provide further documentation about installation and using policies. Some useful links:

• To explore details of writing and implementing policies the Concepts is a good jumping off point.
• For installation into production environments we recommend reviewing Advanced Installations.
• Finally the Use Cases section covers different uses and deployment concerns related to Tetragon.

3 - Installation and Configuration

3.1 - Deploy on Kubernetes

The recommended way to deploy Tetragon on a Kubernetes cluster is to use the Helm chart with Helm 3. Tetragon uses the helm.cilium.io repository to release the helm chart.

Install

To install the latest release of the Tetragon helm chart, use the following command.

helm repo add cilium https://helm.cilium.io
helm repo update
helm install tetragon cilium/tetragon -n kube-system

To wait until Tetragon deployment is ready, use the following kubectl command:

kubectl rollout status -n kube-system ds/tetragon -w

Configuration

You can then make modifications to the Tetragon configuration using helm upgrade, see the following example.

helm upgrade tetragon cilium/tetragon -n kube-system --set tetragon.grpc.address=localhost:1337

You can also edit the tetragon-config ConfigMap directly and restart the Tetragon daemonset with:

kubectl edit cm tetragon-config -n kube-system
kubectl rollout restart ds/tetragon -n kube-system

Upgrade

Upgrade Tetragon using a new specific version of the helm chart.

helm upgrade tetragon cilium/tetragon -n kube-system --version 0.9.0

Uninstall

Uninstall Tetragon using the following command.

helm uninstall tetragon -n kube-system

3.2 - Deploy as a container

Install

Stable versions

To run a stable version, please check Tetragon quay repository and select which version you want. For example if you want to run the latest version which is v1.2.0 currently.

docker run --name tetragon --rm -d                   \
    --pid=host --cgroupns=host --privileged          \
    -v /sys/kernel/btf/vmlinux:/var/lib/tetragon/btf \
    quay.io/cilium/tetragon:v1.2.0

Unstable-development versions

To run unstable development versions of Tetragon, use the latest tag from Tetragon-CI quay repository. This will run the image that was built from the latest commit available on the Tetragon main branch.

docker run --name tetragon --rm -d                  \
   --pid=host --cgroupns=host --privileged          \
   -v /sys/kernel/btf/vmlinux:/var/lib/tetragon/btf \
   quay.io/cilium/tetragon-ci:latest

Configuration

There are multiple ways to set configuration options:

1. Append Tetragon controlling settings at the end of the command

   As an example set the file where to export JSON events with --export-filename argument:

   docker run --name tetragon --rm -d \
       --pid=host --cgroupns=host --privileged \
       -v /sys/kernel:/sys/kernel \
       quay.io/cilium/tetragon:v1.2.0 \
       /usr/bin/tetragon --export-filename /var/log/tetragon/tetragon.log
   

   For a complete list of CLI arguments, please check Tetragon daemon configuration.

2. Environment variables

   docker run --name tetragon --rm -d \
       --pid=host --cgroupns=host --privileged \
       --env "TETRAGON_EXPORT_FILENAME=/var/log/tetragon/tetragon.log" \
       -v /sys/kernel:/sys/kernel \
       quay.io/cilium/tetragon:v1.2.0
   

   Every controlling setting can be set using environment variables. Prefix it with the key word TETRAGON_ then upper case the controlling setting. As an example to set where to export JSON events: --export-filename will be TETRAGON_EXPORT_FILENAME.

   For a complete list of all controlling settings, please check tetragon daemon configuration.

3. Configuration files mounted as volumes

   On the host machine set the configuration drop-ins inside /etc/tetragon/tetragon.conf.d/ directory according to the configuration examples, then mount it as volume:

   docker run --name tetragon --rm -d \
       --pid=host --cgroupns=host --privileged \
       -v /sys/kernel:/sys/kernel \
       -v /etc/tetragon/tetragon.conf.d/:/etc/tetragon/tetragon.conf.d/ \
       quay.io/cilium/tetragon:v1.2.0
   

   This will map the /etc/tetragon/tetragon.conf.d/ drop-in directory from the host into the container.

See Tetragon daemon configuration reference for further details.

3.3 - Deploy with a package

Install

Tetragon will be managed as a systemd service. Tarballs are built and distributed along the assets in the releases.

1. First download the latest binary tarball, using curl for example to download the amd64 release:

   curl -LO https://github.com/cilium/tetragon/releases/download/v1.2.0/tetragon-v1.2.0-amd64.tar.gz
   

2. Extract the downloaded archive, and start the install script to install Tetragon. Feel free to inspect the script before starting it.

   tar -xvf tetragon-v1.2.0-amd64.tar.gz
   cd tetragon-v1.2.0-amd64/
   sudo ./install.sh
   

   If Tetragon was successfully installed, the final output should be similar to:

   Tetragon installed successfully!
   

3. Finally, you can check the Tetragon systemd service.

   sudo systemctl status tetragon
   

   The output should be similar to:

   ● tetragon.service - Tetragon eBPF-based Security Observability and Runtime Enforcement
    Loaded: loaded (/lib/systemd/system/tetragon.service; enabled; vendor preset: enabled)
    Active: active (running) since Mon 2023-01-23 20:08:16 CET; 5s ago
      Docs: https://github.com/cilium/tetragon/
   Main PID: 138819 (tetragon)
     Tasks: 17 (limit: 18985)
    Memory: 151.7M
       CPU: 913ms
    CGroup: /system.slice/tetragon.service
            └─138819 /usr/local/bin/tetragon
   

Configuration

The default Tetragon configuration shipped with the Tetragon package will be installed in /usr/local/lib/tetragon/tetragon.conf.d/. Local administrators can change the configuration by adding drop-ins inside /etc/tetragon/tetragon.conf.d/ to override the default settings or use the command line flags. To restore default settings, remove any added configuration inside /etc/tetragon/tetragon.conf.d/.

See Tetragon daemon configuration for further details.

Upgrade

To upgrade Tetragon:

1. Download the new tarball.

   curl -LO https://github.com/cilium/tetragon/releases/download/v1.2.0/tetragon-v1.2.0-amd64.tar.gz
   

2. Stop the Tetragon service.

   sudo systemctl stop tetragon
   

3. Remove the old Tetragon version.

   sudo rm -fr /usr/lib/systemd/system/tetragon.service
   sudo rm -fr /usr/local/bin/tetragon
   sudo rm -fr /usr/local/lib/tetragon/
   

4. Install the upgraded Tetragon version.

   tar -xvf tetragon-v1.2.0-amd64.tar.gz
   cd tetragon-v1.2.0-amd64/
   sudo ./install.sh
   

Uninstall

To completely remove Tetragon run the uninstall.sh script that is provided inside the tarball.

sudo ./uninstall.sh

Or remove it manually.

sudo systemctl stop tetragon
sudo systemctl disable tetragon
sudo rm -fr /usr/lib/systemd/system/tetragon.service
sudo systemctl daemon-reload
sudo rm -fr /usr/local/bin/tetragon
sudo rm -fr /usr/local/bin/tetra
sudo rm -fr /usr/local/lib/tetragon/

To just purge custom settings:

sudo rm -fr /etc/tetragon/

Operating

gRPC API access

To access the gRPC API with tetra client, set --server-address to point to the corresponding address:

sudo tetra --server-address unix:///var/run/tetragon/tetragon.sock getevents

See restrict gRPC API access for further details.

Tetragon Events

By default JSON events are logged to /var/log/tetragon/tetragon.log unless this location is changed. Logs are always rotated into the same directory.

To read real-time JSON events, tailing the logs file is enough.

sudo tail -f /var/log/tetragon/tetragon.log

Tetragon also ships a gRPC client that can be used to receive events.

1. To print events in json format using tetra gRPC client:

   sudo tetra --server-address "unix:///var/run/tetragon/tetragon.sock" getevents
   

2. To print events in human compact format:

   sudo tetra --server-address "unix:///var/run/tetragon/tetragon.sock" getevents -o compact
   

What’s next

See Explore security observability events to learn more about how to see the Tetragon events.

3.4 - Configure Runtime Hooks

See Tetragon Runtime Hooks, for an introduction to the topic.

Install Tetragon with Runtime Hooks

We use minikube as the example platform because it supports both cri-o and containerd, but the same steps can be applied to other platforms.

Setup Helm

helm repo add cilium https://helm.cilium.io
helm repo update

Setup cluster

• minikube with CRI-O
• minikube with Containerd

minikube start --driver=kvm2 --container-runtime=cri-o

minikube start --driver=kvm2 --container-runtime=cri-o

[plugins."io.containerd.nri.v1.nri"]
  disable = false
  disable_connections = false
  plugin_config_path = "/etc/nri/conf.d"
  plugin_path = "/opt/nri/plugins"
  plugin_registration_timeout = "5s"
  plugin_request_timeout = "2s"
  socket_path = "/var/run/nri/nri.sock"

minikube ssh cat /etc/containerd/config.toml > /tmp/old-config.toml
./contrib/tetragon-rthooks/tetragon-oci-hook-setup patch-containerd-conf enable-nri --config-file=/tmp/old-config.toml --output=/tmp/new-config.toml
diff -u /tmp/old-config.toml /tmp/new-config.toml

--- /tmp/old-config.toml        2024-07-02 11:51:23.893382357 +0200
+++ /tmp/new-config.toml        2024-07-02 11:51:52.841533035 +0200
@@ -67,3 +67,11 @@
     mutation_threshold = 100
     schedule_delay = "0s"
     startup_delay = "100ms"
+  [plugins."io.containerd.nri.v1.nri"]
+    disable = false
+    disable_connections = false
+    plugin_config_path = "/etc/nri/conf.d"
+    plugin_path = "/opt/nri/plugins"
+    plugin_registration_timeout = "5s"
+    plugin_request_timeout = "2s"
+    socket_path = "/var/run/nri/nri.sock"

minikube cp /tmp/new-config.toml /etc/containerd/config.toml
minikube ssh sudo systemctl restart containerd

Install Tetragon

• CRI-O (oci-hooks)
• Containerd (nri-hook)

helm install \
   --namespace kube-system \
   --set rthooks.enabled=true \
   --set rthooks.interface=oci-hooks \
   tetragon ./install/kubernetes/tetragon

helm install \
   --namespace kube-system \
   --set rthooks.enabled=true \
   --set rthooks.interface=nri-hook \
   tetragon ./install/kubernetes/tetragon

kubecl -n kube-system get pods | grep tetragon

With output similar to:

tetragon-hpjwq                       2/2     Running   0          2m42s
tetragon-operator-664ddc8957-9lmd2   1/1     Running   0          2m42s
tetragon-rthooks-m24xr               1/1     Running   0          2m42s

Test Runtime Hooks

Start a pod:

kubectl run test --image=debian  --rm -it -- /bin/bash

Check logs:

minikube ssh 'tail -1 /opt/tetragon/tetragon-oci-hook.log'

Output:

{"time":"2024-07-01T10:57:21.435689144Z","level":"INFO","msg":"hook request to agent succeeded","hook":"create-container","start-time":"2024-07-01T10:57:21.433755984Z","req-cgroups":"/kubepods/besteffort/podd4e74de2-0db8-4143-ae55-695b2489c727/crio-828977b42e3149b502b31708778d0c057efbce038af80d0882ed3e0cb0ff8796","req-rootdir":"/run/containers/storage/overlay-containers/828977b42e3149b502b31708778d0c057efbce038af80d0882ed3e0cb0ff8796/userdata","req-containerName":"test"}

Configuring Runtime Hooks installation

Installation directory (installDir)

For tetragon runtime hooks to work, a binary (tetragon-oci-hook) needs to be installed on the host. Installation happens by the tetragon-rthooks daemonset and the binary is installed in /opt/tetragon by default.

In some systems, however, the /opt directory is mounted read-only. This will result in errors such as:

Warning  FailedMount  8s (x5 over 15s)  kubelet            MountVolume.SetUp failed for volume "oci-hook-install-path" : mkdir /opt/tetragon: read-only file system                                                                                                                                     (6 results) [48/6775]

You can use the rthooks.installDir helm variable to select a different location. For example:

--set rthooks.installDir=/run/tetragon

Failure check (failAllowNamespaces)

By default, tetragon-oci-hook logs information to /opt/tetragon/tetragon-oci-hook.log. Inspecting this file we get the following messages.

{"time":"2024-03-05T15:18:52.669044463Z","level":"WARN","msg":"hook request to the agent failed","hook":"create-container","start-time":"2024-03-05T15:18:42.667916779Z","req-cgroups":"/kubepods/besteffort/pod43ec7f32-3c9f-429f-a01c-fbaafff9f8e1/crio-1d18fd58f0879f6152a1c421f8f1e0987845394ee17001a16bee2df441c112f3","req-rootdir":"/run/containers/storage/overlay-containers/1d18fd58f0879f6152a1c421f8f1e0987845394ee17001a16bee2df441c112f3/userdata","err":"connecting to agent (context deadline exceeded) failed: unix:///var/run/cilium/tetragon/tetragon.sock"}
{"time":"2024-03-05T15:18:52.66912411Z","level":"INFO","msg":"failCheck determined that we should not fail this container, even if there was an error","hook":"create-container","start-time":"2024-03-05T15:18:42.667916779Z"}
{"time":"2024-03-05T15:18:53.01093915Z","level":"WARN","msg":"hook request to the agent failed","hook":"create-container","start-time":"2024-03-05T15:18:43.01005032Z","req-cgroups":"/kubepods/burstable/pod60f971e6-ac38-4aa0-b2d3-549333b2c803/crio-c0bf4e38bfa4ed5c58dd314d505f8b6a0f513d2f2de4dc4aa86a55c7c3e963ab","req-rootdir":"/run/containers/storage/overlay-containers/c0bf4e38bfa4ed5c58dd314d505f8b6a0f513d2f2de4dc4aa86a55c7c3e963ab/userdata","err":"connecting to agent (context deadline exceeded) failed: unix:///var/run/cilium/tetragon/tetragon.sock"}
{"time":"2024-03-05T15:18:53.010999098Z","level":"INFO","msg":"failCheck determined that we should not fail this container, even if there was an error","hook":"create-container","start-time":"2024-03-05T15:18:43.01005032Z"}
{"time":"2024-03-05T15:19:04.034580703Z","level":"WARN","msg":"hook request to the agent failed","hook":"create-container","start-time":"2024-03-05T15:18:54.033449685Z","req-cgroups":"/kubepods/besteffort/pod43ec7f32-3c9f-429f-a01c-fbaafff9f8e1/crio-d95e61f118557afdf3713362b9034231fee9bd7033fc8e7cc17d1efccac6f54f","req-rootdir":"/run/containers/storage/overlay-containers/d95e61f118557afdf3713362b9034231fee9bd7033fc8e7cc17d1efccac6f54f/userdata","err":"connecting to agent (context deadline exceeded) failed: unix:///var/run/cilium/tetragon/tetragon.sock"}
{"time":"2024-03-05T15:19:04.03463995Z","level":"INFO","msg":"failCheck determined that we should not fail this container, even if there was an error","hook":"create-container","start-time":"2024-03-05T15:18:54.033449685Z"}

To understand these messages, consider what tetragon-oci-hook should do if it cannot contact the Tetragon agent.

You may want to stop certain workloads from running. For other workloads (for example, the tetragon pod itself) you probably want to do the opposite and let the them start. To this end, tetragon-oci-hook checks the container annotations, and by default does not fail a container if it belongs in the same namespace as Tetragon. The previous messages concern the tetragon containers (tetragon-operator and tetragon) and they indicate that the choice was made not to fail this container from starting.

Furthermore, users may specify additional namespaces where the container will not fail if the tetragon agent cannot be contacted via the rthooks.failAllowNamespaces option.

For example:

rthooks:
  enabled: true
  failAllowNamespaces: namespace1,namespace2

3.5 - Install tetra CLI

This guide presents various methods to install tetra in your environment.

Install the latest release

Autodetect your environment

This shell script autodetects the OS and the architecture, downloads the archive of the binary and its SHA 256 digest, compares that the actual digest with the supposed one, installs the binary, and removes the download artifacts.

GOOS=$(go env GOOS)
GOARCH=$(go env GOARCH)
curl -L --remote-name-all https://github.com/cilium/tetragon/releases/latest/download/tetra-${GOOS}-${GOARCH}.tar.gz{,.sha256sum}
sha256sum --check tetra-${GOOS}-${GOARCH}.tar.gz.sha256sum
sudo tar -C /usr/local/bin -xzvf tetra-${GOOS}-${GOARCH}.tar.gz
rm tetra-${GOOS}-${GOARCH}.tar.gz{,.sha256sum}

Quick install for each environment

This installation method retrieves the adapted archived for your environment, extract it and install it in the /usr/local/bin directory.

• Linux amd64
• Linux arm64
• macOS amd64
• macOS arm64
• Windows amd64
• Windows arm64

curl -L https://github.com/cilium/tetragon/releases/latest/download/tetra-linux-amd64.tar.gz | tar -xz
sudo mv tetra /usr/local/bin

curl -L https://github.com/cilium/tetragon/releases/latest/download/tetra-linux-arm64.tar.gz | tar -xz
sudo mv tetra /usr/local/bin

curl -L https://github.com/cilium/tetragon/releases/latest/download/tetra-darwin-amd64.tar.gz | tar -xz
sudo mv tetra /usr/local/bin

curl -L https://github.com/cilium/tetragon/releases/latest/download/tetra-darwin-arm64.tar.gz | tar -xz
sudo mv tetra /usr/local/bin

curl -LO https://github.com/cilium/tetragon/releases/latest/download/tetra-windows-amd64.tar.gz
tar -xzf tetra-windows-amd64.tar.gz
# move the binary in a directory in your PATH

curl -LO https://github.com/cilium/tetragon/releases/latest/download/tetra-windows-arm64.tar.gz
tar -xzf tetra-windows-arm64.tar.gz
# move the binary in a directory in your PATH

Install using homebrew

Homebrew is a package manager for macOS and Linux. A formulae is available to fetch precompiled binaries. You can also use it to build from sources (using the --build-from-source flag) with a Go dependency.

brew install tetra

Install a specific release

You can retrieve the release of tetra along the release of Tetragon on GitHub at the following URL: https://github.com/cilium/tetragon/releases.

To download a specific release you can use the following script, replacing the OS, ARCH and TAG values with your desired options.

OS=linux
ARCH=amd64
TAG=v0.9.0
curl -LO https://github.com/cilium/tetragon/releases/download/${TAG}/tetra-${OS}-${ARCH}.tar.gz | tar -xz

3.6 - Verify installation

Verify Tetragon image signature

Learn how to verify Tetragon container images signatures.

Prerequisites

You will need to install cosign.

Verify Signed Container Images

Since version 0.8.4, all Tetragon container images are signed using cosign.

Let’s verify a Tetragon image’s signature using the cosign verify command:

cosign verify --certificate-github-workflow-repository cilium/tetragon --certificate-oidc-issuer https://token.actions.githubusercontent.com <Image URL> | jq

Verify the SBOM signature

Download and verify the signature of the software bill of materials

A Software Bill of Materials (SBOM) is a complete, formally structured list of components that are required to build a given piece of software. SBOM provides insight into the software supply chain and any potential concerns related to license compliance and security that might exist.

Starting with version 0.8.4, all Tetragon images include an SBOM. The SBOM is generated in SPDX format using the bom tool. If you are new to the concept of SBOM, see what an SBOM can do for you.

Download SBOM

The SBOM can be downloaded from the supplied Tetragon image using the cosign download sbom command.

cosign download sbom --output-file sbom.spdx <Image URL>

Verify SBOM Image Signature

To ensure the SBOM is tamper-proof, its signature can be verified using the cosign verify command.

COSIGN_EXPERIMENTAL=1 cosign verify --certificate-github-workflow-repository cilium/tetragon --certificate-oidc-issuer https://token.actions.githubusercontent.com --attachment sbom <Image URL> | jq

It can be validated that the SBOM image was signed using Github Actions in the Cilium repository from the Issuer and Subject fields of the output.

3.7 - Configure Tetragon

Depending on your deployment mode, Tetragon configuration can be changed by:

• Kubernetes
• Docker
• systemd

kubectl edit cm -n kube-system tetragon-config
# Change your configuration setting, save and exit
# Restart Tetragon daemonset
kubectl rollout restart -n kube-system ds/tetragon

# Change configuration inside /etc/tetragon/ then restart container.
# Example:
#   1. As a privileged user, write to the file /etc/tetragon/tetragon.conf.d/export-file
#      the path where to export events, example "/var/log/tetragon/tetragon.log"
#   2. Bind mount host /etc/tetragon into container /etc/tetragon
# Tetragon events will be exported to /var/log/tetragon/tetragon.log
echo "/var/log/tetragon/tetragon.log" > /etc/tetragon/tetragon.conf.d/export-file
docker run --name tetragon --rm -d \
  --pid=host --cgroupns=host --privileged \
  -v /etc/tetragon:/etc/tetragon \
  -v /sys/kernel:/sys/kernel \
  -v /var/log/tetragon:/var/log/tetragon \
  quay.io/cilium/tetragon:v1.2.0 \
  /usr/bin/tetragon

# Change configuration inside /etc/tetragon/ then restart systemd service.
# Example:
#   1. As a privileged user, write to the file /etc/tetragon/tetragon.conf.d/export-file
#      the path where to export events, example "/var/log/tetragon/tetragon.log"
#   2. Bind mount host /etc/tetragon into container /etc/tetragon
# Tetragon events will be exported to /var/log/tetragon/tetragon.log
echo "/var/log/tetragon/tetragon.log" > /etc/tetragon/tetragon.conf.d/export-file
systemctl restart tetragon

To read more about Tetragon configuration, please check our reference pages:

• For Kubernetes deployments, see the Helm chart reference.
• For Container or systemd deployments, see the Daemon configuration reference.

Enable Process Credentials

On Linux each process has various associated user, group IDs and capabilities known as process credentials. To enable visility into process_credentials, run Tetragon with enable-process-creds setting set.

• Kubernetes
• Docker
• systemd

kubectl edit cm -n kube-system tetragon-config
# Change "enable-process-cred" from "false" to "true", then save and exit
# Restart Tetragon daemonset
kubectl rollout restart -n kube-system ds/tetragon

echo "true" > /etc/tetragon/tetragon.conf.d/enable-process-cred
docker run --name tetragon --rm -d \
  --pid=host --cgroupns=host --privileged \
  -v /etc/tetragon:/etc/tetragon \
  -v /sys/kernel:/sys/kernel \
  -v /var/log/tetragon:/var/log/tetragon \
  quay.io/cilium/tetragon:v1.2.0 \
  /usr/bin/tetragon

# Write to the drop-in file /etc/tetragon/tetragon.conf.d/enable-process-cred  true
# Run the following as a privileged user then restart tetragon service
echo "true" > /etc/tetragon/tetragon.conf.d/enable-process-cred
systemctl restart tetragon

3.8 - Metrics

Tetragon exposes a number of Prometheus metrics that can be used for two main purposes:

1. Monitoring the health of Tetragon itself
2. Monitoring the activity of processes observed by Tetragon

For the full list, refer to metrics reference.

Enable/Disable Metrics

Kubernetes

In a Kubernetes installation, metrics are enabled by default and exposed via the endpoint /metrics. The tetragon service exposes the Tetragon Agent metrics on port 2112, and the tetragon-operator-metrics service the Tetragon Operator metrics on port 2113.

You can change the port via Helm values:

tetragon:
  prometheus:
    port: 2222 # default is 2112
tetragonOperator:
  prometheus:
    port: 3333 # default is 2113

Or entirely disable the metrics server:

tetragon:
  prometheus:
    enabled: false # default is true
tetragonOperator:
  prometheus:
    enabled: false # default is true

Non-Kubernetes

In a non-Kubernetes installation, metrics are disabled by default. You can enable them by setting the metrics server address of the Tetragon Agent to, for example, :2112, via the --metrics-server flag.

If using systemd, set the metrics-address entry in a file under the /etc/tetragon/tetragon.conf.d/ directory.

Verify that metrics are exposed

To verify that the metrics server has started, check the logs of the Tetragon components. Here’s an example for the Tetragon Agent, running on Kubernetes:

kubectl -n <tetragon-namespace> logs ds/tetragon

The logs should contain a line similar to the following:

time="2023-09-22T23:16:24+05:30" level=info msg="Starting metrics server" addr="localhost:2112"

To see what metrics are exposed, you can access the metrics endpoint directly. In Kubernetes, forward the metrics port:

kubectl -n <tetragon-namespace> port-forward svc/tetragon 2112:2112

Access localhost:2112/metrics endpoint either in a browser or for example using curl. You should see a list of metrics similar to the following:

# HELP promhttp_metric_handler_errors_total Total number of internal errors encountered by the promhttp metric handler.
# TYPE promhttp_metric_handler_errors_total counter
promhttp_metric_handler_errors_total{cause="encoding"} 0
promhttp_metric_handler_errors_total{cause="gathering"} 0
# HELP tetragon_errors_total The total number of Tetragon errors. For internal use only.
# TYPE tetragon_errors_total counter
[...]

Configure labels on events metrics

Depending on the workloads running in the environment, Events Metrics may have very high cardinality. This is particularly likely in Kubernetes environments, where each pod creates a separate timeseries. To avoid overwhelming Prometheus, Tetragon provides an option to choose which labels are populated in these metrics.

You can configure the labels via Helm values or the --metrics-label-filter flag. Set the value to a comma-separated list of enabled labels:

tetragon:
  prometheus:
    metricsLabelFilter: "namespace,workload,binary" # "pod" label is disabled

Enable Prometheus ServiceMonitors

Typically, metrics are scraped by Prometheus or another compatible agent (for example OpenTelemetry Collector), stored in Prometheus or another compatible database, then queried and visualized for example using Grafana.

In Kubernetes, you can install Prometheus and Grafana using the Kube-Prometheus-Stack Helm chart. This Helm chart includes the Prometheus Operator, which allows you to configure Prometheus via Kubernetes custom resources.

helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
helm install kube-prometheus-stack prometheus-community/kube-prometheus-stack \
  --namespace monitoring \
  --create-namespace \
  --set prometheus.prometheusSpec.serviceMonitorSelectorNilUsesHelmValues=false

Refer to the official Kube-Prometheus-Stack documentation for more details.

Tetragon comes with default ServiceMonitor resources containing the scrape confguration for the Agent and Operator. You can enable it via Helm values:

tetragon:
  prometheus:
    serviceMonitor:
      enabled: true
tetragonOperator:
  prometheus:
    serviceMonitor:
      enabled: true

To ensure that Prometheus has detected the Tetragon metrics endpoints, you can check the Prometheus targets:

1. Access the Prometheus UI.
2. Navigate to the “Status” tab and select “Targets”.
3. Verify that the Tetragon metric endpoints are listed and their status is UP.

3.9 - FAQ

What is the minimum Linux kernel version to run Tetragon?

Tetragon needs Linux kernel version 4.19 or greater.

We currently run tests on stable long-term support kernels 4.19, 5.4, 5.10, 5.15 and bpf-next, see this test workflow for up to date information. Not all Tetragon features work with older kernel versions. BPF evolves rapidly and we recommend you use the most recent stable kernel possible to get the most out of Tetragon’s features.

Note that Tetragon needs BTF support which might take some work on older kernels.

What are the Linux kernel configuration options needed to run Tetragon?

This is the list of needed configuration options, note that this might evolve quickly with new Tetragon features:

# CORE BPF
CONFIG_BPF
CONFIG_BPF_JIT
CONFIG_BPF_JIT_DEFAULT_ON
CONFIG_BPF_EVENTS
CONFIG_BPF_SYSCALL
CONFIG_HAVE_BPF_JIT
CONFIG_HAVE_EBPF_JIT
CONFIG_FTRACE_SYSCALLS

# BTF
CONFIG_DEBUG_INFO_BTF
CONFIG_DEBUG_INFO_BTF_MODULES

# Enforcement
CONFIG_BPF_KPROBE_OVERRIDE

# CGROUP and Process tracking
CONFIG_CGROUPS=y        Control Group support
CONFIG_MEMCG=y          Memory Control group
CONFIG_BLK_CGROUP=y     Generic block IO controller
CONFIG_CGROUP_SCHED=y
CONFIG_CGROUP_PIDS=y    Process Control group
CONFIG_CGROUP_FREEZER=y Freeze and unfreeze tasks controller
CONFIG_CPUSETS=y        Manage CPUSETs
CONFIG_PROC_PID_CPUSET=y
CONFIG_CGROUP_DEVICE=Y  Devices Control group
CONFIG_CGROUP_CPUACCT=y CPU accouting controller
CONFIG_CGROUP_PERF=y
CONFIG_CGROUP_BPF=y     Attach eBPF programs to a cgroup
CGROUP_FAVOR_DYNMODS=y  (optional)  >= 6.0
  Reduces the latencies of dynamic cgroup modifications at the
  cost of making hot path operations such as forks and exits
  more expensive.
  Platforms with frequent cgroup migrations could enable this
  option as a potential alleviation for pod and containers
  association issues.

At runtime, to probe if your kernel has sufficient features turned on, you can run tetra with root privileges with the probe command:

sudo tetra probe

You can also run this command directly from the tetragon container image on a Kubernetes cluster node. For example:

kubectl run bpf-probe --image=quay.io/cilium/tetragon-ci:latest --privileged --restart=Never -it --rm --command -- tetra probe

The output should be similar to this (with boolean values depending on your actual configuration):

override_return: true
buildid: true
kprobe_multi: false
fmodret: true
fmodret_syscall: true
signal: true
large: true

Tetragon failed to start complaining about a missing BTF file

You might have encountered the following issues:

level=info msg="BTF discovery: default kernel btf file does not exist" btf-file=/sys/kernel/btf/vmlinux
level=info msg="BTF discovery: candidate btf file does not exist" btf-file=/var/lib/tetragon/metadata/vmlinux-5.15.49-linuxkit
level=info msg="BTF discovery: candidate btf file does not exist" btf-file=/var/lib/tetragon/btf
[...]
level=fatal msg="Failed to start tetragon" error="tetragon, aborting kernel autodiscovery failed: Kernel version \"5.15.49-linuxkit\" BTF search failed kernel is not included in supported list. Please check Tetragon requirements documentation, then use --btf option to specify BTF path and/or '--kernel' to specify kernel version"

Tetragon needs BTF (BPF Type Format) support to load its BPF programs using CO-RE (Compile Once - Run Everywhere). In brief, CO-RE is useful to load BPF programs that have been compiled on a different kernel version than the target kernel. Indeed, kernel structures change between versions and BPF programs need to access fields in them. So CO-RE uses the BTF file of the kernel in which you are loading the BPF program to know the differences between the struct and patch the fields offset in the accessed structures. CO-RE allows portability of the BPF programs but requires a kernel with BTF enabled.

Most of the common Linux distributions now ship with BTF enabled and do not require any extra work, this is kernel option CONFIG_DEBUG_INFO_BTF=y. To check if BTF is enabled on your Linux system and see the BTF data file of your kernel, the standard location is /sys/kernel/btf/vmlinux. By default, Tetragon will look for this file (this is the first line in the log output above).

If your kernel does not support BTF you can:

• Retrieve the BTF file for your kernel version from an external source.
• Build the BTF file from your kernel debug symbols. You will need pahole to add BTF metadata to the debugging symbols and LLVM minimize the medata size.
• Rebuild your kernel with CONFIG_DEBUG_INFO_BTF to y.

Tetragon will also look into /var/lib/tetragon/btf for the vmlinux file (this is the third line in the log output above). Or you can use the --btf flag to directly indicate Tetragon where to locate the file.

If you encounter this issue while using Docker Desktop on macOS, please refer to can I run Tetragon on Mac computers.

Can I install and use Tetragon in standalone mode (outside of k8s)?

Yes! Refer to the Container or Package installation guides.

Otherwise you can build Tetragon from source by running make to generate standalone binaries. Make sure to take a look at the Development Setup guide for the build requirements. Then use sudo ./tetragon --bpf-lib bpf/objs to run Tetragon.

Can I run Tetragon on Mac computers?

Yes! You can run Tetragon locally by running a Linux virtual machine on your Mac.

On macOS running on amd64 (also known as Intel Mac) and arm64 (also know as Apple Silicon Mac), open source and commercial solutions exists to run virtual machines, here is a list of popular open source projects that you can use:

• Lima: Linux virtual machines: website lima-vm.io.
• UTM: Virtual machines for iOS and macOS: website mac.getutm.app.
• VirtualBox: website virtualbox.org (arm64 in developer preview).

You can use these solutions to run a recent Linux distribution that ships with BTF debug information support.

Please note that you need to use a recent Docker Desktop version on macOS (for example 24.0.6 with Kernel 6.4.16-linuxkit), because the Linux virtual machine provided by older Docker Desktop versions lacked support for the BTF debug information. The BTF debug information file is needed for CO-RE in order to load sensors of Tetragon. Run the following commands to see if Tetragon can be used on your Docker Desktop version:

# The Kernel needs to be compiled with CONFIG_DEBUG_INFO_BTF and
# CONFIG_DEBUG_INFO_BTF_MODULES support:
$ docker run -it --rm --privileged --pid=host ubuntu \
    nsenter -t 1 -m -u -n -i sh -c \
    'cat /proc/config.gz | gunzip | grep CONFIG_DEBUG_INFO_BTF'
CONFIG_DEBUG_INFO_BTF=y
CONFIG_DEBUG_INFO_BTF_MODULES=y

# "/sys/kernel/btf/vmlinux" should be present:
$ docker run -it --rm --privileged --pid=host ubuntu \
    nsenter -t 1 -m -u -n -i sh -c 'ls -la /sys/kernel/btf/vmlinux'
-r--r--r--    1 root     root       4988627 Nov 21 20:33 /sys/kernel/btf/vmlinux

4 - Concepts

4.1 - Events

Tetragon’s events are exposed to the system through either the gRPC endpoint or JSON logs. Commands in this section assume the Getting Started guide was used, but are general other than the namespaces chosen and should work in most environments.

JSON

The first way is to observe the raw json output from the stdout container log:

kubectl logs -n kube-system -l app.kubernetes.io/name=tetragon -c export-stdout -f

The raw JSON events provide Kubernetes API, identity metadata, and OS level process visibility about the executed binary, its parent and the execution time. A base Tetragon installation will produce process_exec and process_exit events encoded in JSON as shown here,

{
  "process_exec": {
    "process": {
      "exec_id": "Z2tlLWpvaG4tNjMyLWRlZmF1bHQtcG9vbC03MDQxY2FjMC05czk1OjEzNTQ4Njc0MzIxMzczOjUyNjk5",
      "pid": 52699,
      "uid": 0,
      "cwd": "/",
      "binary": "/usr/bin/curl",
      "arguments": "https://ebpf.io/applications/#tetragon",
      "flags": "execve rootcwd",
      "start_time": "2023-10-06T22:03:57.700327580Z",
      "auid": 4294967295,
      "pod": {
        "namespace": "default",
        "name": "xwing",
        "container": {
          "id": "containerd://551e161c47d8ff0eb665438a7bcd5b4e3ef5a297282b40a92b7c77d6bd168eb3",
          "name": "spaceship",
          "image": {
            "id": "docker.io/tgraf/netperf@sha256:8e86f744bfea165fd4ce68caa05abc96500f40130b857773186401926af7e9e6",
            "name": "docker.io/tgraf/netperf:latest"
          },
          "start_time": "2023-10-06T21:52:41Z",
          "pid": 49
        },
        "pod_labels": {
          "app.kubernetes.io/name": "xwing",
          "class": "xwing",
          "org": "alliance"
        },
        "workload": "xwing"
      },
      "docker": "551e161c47d8ff0eb665438a7bcd5b4",
      "parent_exec_id": "Z2tlLWpvaG4tNjMyLWRlZmF1bHQtcG9vbC03MDQxY2FjMC05czk1OjEzNTQ4NjcwODgzMjk5OjUyNjk5",
      "tid": 52699
    },
    "parent": {
      "exec_id": "Z2tlLWpvaG4tNjMyLWRlZmF1bHQtcG9vbC03MDQxY2FjMC05czk1OjEzNTQ4NjcwODgzMjk5OjUyNjk5",
      "pid": 52699,
      "uid": 0,
      "cwd": "/",
      "binary": "/bin/bash",
      "arguments": "-c \"curl https://ebpf.io/applications/#tetragon\"",
      "flags": "execve rootcwd clone",
      "start_time": "2023-10-06T22:03:57.696889812Z",
      "auid": 4294967295,
      "pod": {
        "namespace": "default",
        "name": "xwing",
        "container": {
          "id": "containerd://551e161c47d8ff0eb665438a7bcd5b4e3ef5a297282b40a92b7c77d6bd168eb3",
          "name": "spaceship",
          "image": {
            "id": "docker.io/tgraf/netperf@sha256:8e86f744bfea165fd4ce68caa05abc96500f40130b857773186401926af7e9e6",
            "name": "docker.io/tgraf/netperf:latest"
          },
          "start_time": "2023-10-06T21:52:41Z",
          "pid": 49
        },
        "pod_labels": {
          "app.kubernetes.io/name": "xwing",
          "class": "xwing",
          "org": "alliance"
        },
        "workload": "xwing"
      },
      "docker": "551e161c47d8ff0eb665438a7bcd5b4",
      "parent_exec_id": "Z2tlLWpvaG4tNjMyLWRlZmF1bHQtcG9vbC03MDQxY2FjMC05czk1OjEzNTQ4NjQ1MjQ1ODM5OjUyNjg5",
      "tid": 52699
    }
  },
  "node_name": "gke-john-632-default-pool-7041cac0-9s95",
  "time": "2023-10-06T22:03:57.700326678Z"
}

Will only highlight a few important fields here. For a full specification of events see the Reference section. All events in Tetragon contain a process_exec block to identify the process generating the event. For execution events this is the primary block. For Tracing Policy events the hook that generated the event will attach further data to this. The process_exec event provides a cluster wide unique id the process_exec.exec_id for this process along with the metadata expected in a Kubernetes cluster process_exec.process.pod. The binary and args being executed are part of the event here process_exec.process.binary and process_exec.process.args. Finally, a node_name and time provide the location and time for the event and will be present in all event types.

A default deployment writes the JSON log to /var/run/cilium/tetragon/tetragon.log where it can be exported through normal log collection tooling, e.g. ‘fluentd’, logstash, etc.. The file will be rotated and compressed by default. See [Helm Options] for details on how to customize this location.

Export Filtering

Export filters restrict the JSON event output to a subset of desirable events. These export filters are configured as a line-separated list of JSON objects, where each object can contain one or more filter expressions. Filters are combined by taking the logical OR of each line-separated filter object and the logical AND of sibling expressions within a filter object. As a concrete example, suppose we had the following filter configuration:

{"event_set": ["PROCESS_EXEC", "PROCESS_EXIT"], "namespace": "foo"}
{"event_set": ["PROCESS_KPROBE"]}

The above filter configuration would result in a match if:

• The event type is PROCESS_EXEC or PROCESS_EXIT AND the pod namespace is “foo”; OR
• The event type is PROCESS_KPROBE

Tetragon supports two groups of export filters: an allowlist and a denylist. If neither is configured, all events are exported. If only an allowlist is configured, event exports are considered default-deny, meaning only the events in the allowlist are exported. The denylist takes precedence over the allowlist in cases where two filter configurations match on the same event.

You can configure export filters using the provided helm options, command line flags, or environment variables.

List of Process Event Filters

Field Filtering

In some cases, it is not desirable to include all of the fields exported in Tetragon events by default. In these cases, you can use field filters to restrict the set of exported fields for a given event type. Field filters are configured similarly to export filters, as line-separated lists of JSON objects.

Field filters select fields using the protobuf field mask syntax under the "fields" key. You can define a path of fields using field names separated by period (.) characters. To define multiple paths in a single field filter, separate them with comma (,) characters. For example, "fields":"process.binary,parent.binary,pod.name" would select only the process.binary, parent.binary, and pod.name fields.

By default, a field filter applies to all process events, although you can control this behaviour with the "event_set" key. For example, you can apply a field filter to PROCESS_CONNECT and PROCESS_CLOSE events by specifying "event_set":["PROCESS_CONNECT","PROCESS_CLOSE"] in the filter definition.

Each field filter has an "action" that determines what the filter should do with the selected field. The supported action types are "INCLUDE" and "EXCLUDE". A value of "INCLUDE" will cause the field to appear in an event, while a value of "EXCLUDE" will hide the field. In the absence of any field filter for a given event type, the export will include all fields by default. Defining one or more "INCLUDE" filters for a given event type changes that behaviour to exclude all other event types by default.

As a simple example of the above, consider the case where we want to include only exec_id and parent_exec_id in all event types except for PROCESS_EXEC:

{"fields":"process.exec_id,process.parent_exec_id", "event_set": ["PROCESS_EXEC"], "invert_event_set": true, "action": "INCLUDE"}

Redacting Sensitive Information

Since Tetragon traces the entire system, event exports might sometimes contain sensitive information (for example, a secret passed via a command line argument to a process). To prevent this information from being exfiltrated via Tetragon JSON export, Tetragon provides a mechanism called Redaction Filters which can be used to string patterns to redact from exported process arguments. These filters are written in JSON and passed to the Tetragon agent via the --redaction-filters command line flag or the redactionFilters Helm value.

To perform redactions, redaction filters define RE2 regular expressions in the redact field. Any capture groups in these RE2 regular expressions are redacted and replaced with "*****".

For more control, you can select which binary or binaries should have their arguments redacted with the binary_regex field.

As a concrete example, the following will redact all passwords passed to processes with the "--password" argument:

{"redact": ["--password(?:\\s+|=)(\\S*)"]}

Now, an event that contains the string "--password=foo" would have that string replaced with "--password=*****".

Suppose we also see some passwords passed via the -p shorthand for a specific binary, foo. We can also redact these as follows:

{"binary_regex": ["(?:^|/)foo$"], "redact": ["-p(?:\\s+|=)(\\S*)"]}

With both of the above redaction filters in place, we are now redacting all password arguments.

tetra CLI

A second way is to use the tetra CLI. This has the advantage that it can also be used to filter and pretty print the output. The tool allows filtering by process, pod, and other fields. To install tetra see the Tetra Installation Guide

To start printing events run:

kubectl logs -n kube-system -l app.kubernetes.io/name=tetragon -c export-stdout -f | tetra getevents -o compact

The tetra CLI is also available inside tetragon container.

kubectl exec -it -n kube-system ds/tetragon -c tetragon -- tetra getevents -o compact

This was used in the quick start and generates a pretty printing of the events, To further filter by a specific binary and/or pod do the following,

kubectl logs -n kube-system -l app.kubernetes.io/name=tetragon -c export-stdout -f | tetra getevents -o compact --processes curl --pod xwing

Will filter and report just the relevant events.

🚀 process default/xwing /usr/bin/curl https://ebpf.io/applications/#tetragon
💥 exit    default/xwing /usr/bin/curl https://ebpf.io/applications/#tetragon 60

gRPC

In addition Tetragon can expose a gRPC endpoint listeners may attach to. The gRPC is exposed by default helm install on localhost:54321, but the address can be configured with the --server-address option. This can be set from helm with the tetragon.grpc.address flag or disabled completely if needed with tetragon.grpc.enabled.

helm install tetragon cilium/tetragon -n kube-system --set tetragon.grpc.enabled=true --set tetragon.grpc.address=localhost:54321

An example gRPC endpoint is the Tetra CLI when its not piped JSON output directly,

 kubectl exec -ti -n kube-system ds/tetragon -c tetragon -- tetra getevents -o compact

4.2 - Tracing Policy

Tetragon’s TracingPolicy is a user-configurable Kubernetes custom resource (CR) that allows users to trace arbitrary events in the kernel and optionally define actions to take on a match. Policies consist of a hook point (kprobes, tracepoints, and uprobes are supported), and selectors for in-kernel filtering and specifying actions. For more details, see hook points page and the selectors page.

For the complete custom resource definition (CRD) refer to the YAML file cilium.io_tracingpolicies.yaml. One practical way to explore the CRD is to use kubectl explain against a Kubernetes API server on which it is installed, for example kubectl explain tracingpolicy.spec.kprobes provides field-specific documentation and details on kprobe spec.

Tracing Policies can be loaded and unloaded at runtime in Tetragon, or on startup using flags.

• With Kubernetes, you can use kubectl to add and remove a TracingPolicy.
• You can use tetra gRPC CLI to add and remove a TracingPolicy.
• You can use the --tracing-policy and --tracing-policy-dir flags to statically add policies at startup time, see more in the daemon configuration page.

Hence, even though Tracing Policies are structured as a Kubernetes CR, they can also be used in non-Kubernetes environments using the last two loading methods.

4.2.1 - Example

To discover TracingPolicy, let’s understand via an example that will be explained, part by part, in this document:

apiVersion: cilium.io/v1alpha1
kind: TracingPolicy
metadata:
  name: "fd-install"
spec:
  kprobes:
  - call: "fd_install"
    syscall: false
    args:
    - index: 0
      type: "int"
    - index: 1
      type: "file"
    selectors:
    - matchArgs:
      - index: 1
        operator: "Equal"
        values:
        - "/tmp/tetragon"
      matchActions:
      - action: Sigkill

The policy checks for file descriptors being created, and sends a SIGKILL signal to any process that creates a file descriptor to a file named /tmp/tetragon. We discuss the policy in more detail next.

Required fields

apiVersion: cilium.io/v1alpha1
kind: TracingPolicy
metadata:
  name: "fd-install"

The first part follows a common pattern among all Cilium Policies or more widely Kubernetes object. It first declares the Kubernetes API used, then the kind of Kubernetes object it is in this API and an arbitrary name for the object that has to comply with Kubernetes naming convention.

Hook point

spec:
  kprobes:
  - call: "fd_install"
    syscall: false
    args:
    - index: 0
      type: "int"
    - index: 1
      type: "file"

The beginning of the specification describes the hook point to use. Here we are using a kprobe, hooking on the kernel function fd_install. That’s the kernel function that gets called when a new file descriptor is created. We indicate that it’s not a syscall, but a regular kernel function. We then specify the function arguments, so that Tetragon’s BPF code will extract and optionally perform filtering on them.

See the hook points page for further information on the various hook points available and arguments.

Selectors

    selectors:
    - matchArgs:
      - index: 1
        operator: "Equal"
        values:
        - "/tmp/tetragon"
      matchActions:
      - action: Sigkill

Selectors allow you to filter on the events to extract only a subset of the events based on different properties and optionally take an action.

In the example, we filter on the argument at index 1, passing a file struct to the function. Tetragon has the knowledge on how to apply the Equal operator over a Linux kernel file struct and match on the path of the file.

Then we add the Sigkill action, meaning, that any match of the selector should send a SIGKILL signal to the process that initiated the event.

Learn more about the various selectors in the dedicated selectors page.

Message

The message field is an optional short message that will be included in the generated event to inform users what is happening.

spec:
  kprobes:
  - call: "fd_install"
    message: "Installing a file descriptor"

Tags

Tags are optional fields of a Tracing Policy that are used to categorize generated events. Further reference here: Tags documentation.

Policy effect

First, let’s create the /tmp/tetragon file with some content:

echo eBPF! > /tmp/tetragon

You can save the policy in an example.yaml file, compile Tetragon locally, and start Tetragon:

sudo ./tetragon --bpf-lib bpf/objs --tracing-policy example.yaml

(See Quick Kubernetes Install and Quick Local Docker Install for other ways to start Tetragon.)

Once the Tetragon starts, you can monitor events using tetra, the tetragon CLI:

./tetra tetra getevents -o compact

Reading the /tmp/tetragon file with cat:

cat /tmp/tetragon

Results in the following events:

🚀 process  /usr/bin/cat /tmp/tetragon
📬 open     /usr/bin/cat /tmp/tetragon
💥 exit     /usr/bin/cat /tmp/tetragon SIGKILL

And the shell where the cat command was performed will return:

Killed

See more

For more examples of tracing policies, take a look at the examples/tracingpolicy folder in the Tetragon repository. Also read the following sections on hook points and selectors.

4.2.2 - Hook points

Tetragon can hook into the kernel using kprobes and tracepoints, as well as in user-space programs using uprobes. Users can configure these hook points using the correspodning sections of the TracingPolicy specification (.spec). These hook points include arguments and return values that can be specified using the args and returnArg fields as detailed in the following sections.

Kprobes

Kprobes enables you to dynamically hook into any kernel function and execute BPF code. Because kernel functions might change across versions, kprobes are highly tied to your kernel version and, thus, might not be portable across different kernels.

Conveniently, you can list all kernel symbols reading the /proc/kallsyms file. For example to search for the write syscall kernel function, you can execute sudo grep sys_write /proc/kallsyms, the output should be similar to this, minus the architecture specific prefixes.

ffffdeb14ea712e0 T __arm64_sys_writev
ffffdeb14ea73010 T ksys_write
ffffdeb14ea73140 T __arm64_sys_write
ffffdeb14eb5a460 t proc_sys_write
ffffdeb15092a700 d _eil_addr___arm64_sys_writev
ffffdeb15092a740 d _eil_addr___arm64_sys_write

You can see that the exact name of the symbol for the write syscall on our kernel version is __arm64_sys_write. Note that on x86_64, the prefix would be __x64_ instead of __arm64_.

In our example, we will explore a kprobe hooking into the fd_install kernel function. The fd_install kernel function is called each time a file descriptor is installed into the file descriptor table of a process, typically referenced within system calls like open or openat. Hooking fd_install has its benefits and limitations, which are out of the scope of this guide.

spec:
  kprobes:
  - call: "fd_install"
    syscall: false

Kprobes calls can be defined independently in different policies, or together in the same Policy. For example, we can define trace multiple kprobes under the same tracing policy:

spec:
  kprobes:
  - call: "sys_read"
    syscall: true
    # [...]
  - call: "sys_write"
    syscall: true
    # [...]

Tracepoints

Tracepoints are statically defined in the kernel and have the advantage of being stable across kernel versions and thus more portable than kprobes.

To see the list of tracepoints available on your kernel, you can list them using sudo ls /sys/kernel/debug/tracing/events, the output should be similar to this.

alarmtimer    ext4            iommu           page_pool     sock
avc           fib             ipi             pagemap       spi
block         fib6            irq             percpu        swiotlb
bpf_test_run  filelock        jbd2            power         sync_trace
bpf_trace     filemap         kmem            printk        syscalls
bridge        fs_dax          kvm             pwm           task
btrfs         ftrace          libata          qdisc         tcp
cfg80211      gpio            lock            ras           tegra_apb_dma
cgroup        hda             mctp            raw_syscalls  thermal
clk           hda_controller  mdio            rcu           thermal_power_allocator
cma           hda_intel       migrate         regmap        thermal_pressure
compaction    header_event    mmap            regulator     thp
cpuhp         header_page     mmap_lock       rpm           timer
cros_ec       huge_memory     mmc             rpmh          tlb
dev           hwmon           module          rseq          tls
devfreq       i2c             mptcp           rtc           udp
devlink       i2c_slave       napi            sched         vmscan
dma_fence     initcall        neigh           scmi          wbt
drm           interconnect    net             scsi          workqueue
emulation     io_uring        netlink         signal        writeback
enable        iocost          oom             skb           xdp
error_report  iomap           page_isolation  smbus         xhci-hcd

You can then choose the subsystem that you want to trace, and look the tracepoint you want to use and its format. For example, if we choose the netif_receive_skb tracepoints from the net subsystem, we can read its format with sudo cat /sys/kernel/debug/tracing/events/net/netif_receive_skb/format, the output should be similar to the following.

name: netif_receive_skb
ID: 1398
format:
        field:unsigned short common_type;       offset:0;       size:2; signed:0;
        field:unsigned char common_flags;       offset:2;       size:1; signed:0;
        field:unsigned char common_preempt_count;       offset:3;       size:1; signed:0;
        field:int common_pid;   offset:4;       size:4; signed:1;

        field:void * skbaddr;   offset:8;       size:8; signed:0;
        field:unsigned int len; offset:16;      size:4; signed:0;
        field:__data_loc char[] name;   offset:20;      size:4; signed:0;

print fmt: "dev=%s skbaddr=%px len=%u", __get_str(name), REC->skbaddr, REC->len

Similarly to kprobes, tracepoints can also hook into system calls. For more details, see the raw_syscalls and syscalls subysystems.

An example of tracepoints TracingPolicy could be the following, observing all syscalls and getting the syscall ID from the argument at index 4:

apiVersion: cilium.io/v1alpha1
kind: TracingPolicy
metadata:
  name: "raw-syscalls"
spec:
  tracepoints:
  - subsystem: "raw_syscalls"
    event: "sys_enter"
    args:
    - index: 4
      type: "int64"

Uprobes

Uprobes are similar to kprobes, but they allow you to dynamically hook into any user-space function and execute BPF code. Uprobes are also tied to the binary version of the user-space program, so they may not be portable across different versions or architectures.

To use uprobes, you need to specify the path to the executable or library file, and the symbol of the function you want to probe. You can use tools like objdump, nm, or readelf to find the symbol of a function in a binary file. For example, to find the readline symbol in /bin/bash using nm, you can run:

nm -D /bin/bash | grep readline

The output should look similar to this, with a few lines redacted:

[...]
000000000009f2b0 T pcomp_set_readline_variables
0000000000097e40 T posix_readline_initialize
00000000000d5690 T readline
00000000000d52f0 T readline_internal_char
00000000000d42d0 T readline_internal_setup
[...]

You can see in the nm output: first the symbol value, then the symbol type, for the readline symbol T meaning that this symbol is in the text (code) section of the binary, and finally the symbol name. This confirms that the readline symbol is present in the /bin/bash binary and might be a function name that we can hook with a uprobe.

You can define multiple uprobes in the same policy, or in different policies. You can also combine uprobes with kprobes and tracepoints to get a comprehensive view of the system behavior.

Here is an example of a policy that defines an uprobe for the readline function in the bash executable, and applies it to all processes that use the bash binary:

apiVersion: cilium.io/v1alpha1
kind: TracingPolicy
metadata:
  name: "example-uprobe"
spec:
  uprobes:
  - path: "/bin/bash"
    symbols:
    - "readline"

This example shows how to use uprobes to hook into the readline function running in all the bash shells.

LSM BPF

LSM BPF programs allow runtime instrumentation of the LSM hooks by privileged users to implement system-wide MAC (Mandatory Access Control) and Audit policies using eBPF.

List of LSM hooks which can be instrumented can be found in security/security.c.

To verify if BPF LSM is available use the following command:

cat /boot/config-$(uname -r) | grep BPF_LSM

The output should be similar to this if BPF LSM is supported:

CONFIG_BPF_LSM=y

Then, if provided above conditions are met, use this command to check if BPF LSM is enabled:

cat /sys/kernel/security/lsm

The output might look like this:

bpf,lockdown,integrity,apparmor

If the output includes the bpf, than BPF LSM is enabled. Otherwise, you can modify /etc/default/grub:

GRUB_CMDLINE_LINUX="lsm=lockdown,integrity,apparmor,bpf"

Then, update the grub configuration and restart the system.

The provided example of LSM BPF TracingPolicy monitors access to files /etc/passwd and /etc/shadow with /usr/bin/cat executable.

apiVersion: cilium.io/v1alpha1
kind: TracingPolicy
metadata:
  name: "lsm-file-open"
spec:
  lsmhooks:
  - hook: "file_open"
    args:
      - index: 0
        type: "file"
    selectors:
    - matchBinaries:
      - operator: "In"
        values:
        - "/usr/bin/cat"
      matchArgs:
      - index: 0
        operator: "Equal"
        values:
        - "/etc/passwd"
        - "/etc/shadow"

Arguments

Kprobes, uprobes and tracepoints all share a needed arguments fields called args. It is a list of arguments to include in the trace output. Tetragon’s BPF code requires information about the types of arguments to properly read, print and filter on its arguments. This information needs to be provided by the user under the args section. For the available types, check the TracingPolicy CRD.

Following our example, here is the part that defines the arguments:

args:
- index: 0
  type: "int"
- index: 1
  type: "file"

Each argument can optionally include a ’label’ parameter, which will be included in the output. This can be used to annotate the arguments to help with understanding and processing the output. As an example, here is the same definition, with an appropriate label on the int argument:

args:
- index: 0
  type: "int"
  label: "FD"
- index: 1
  type: "file"

To properly read and hook onto the fd_install(unsigned int fd, struct file *file) function, the YAML snippet above tells the BPF code that the first argument is an int and the second argument is a file, which is the struct file of the kernel. In this way, the BPF code and its printer can properly collect and print the arguments.

These types are sorted by the index field, where you can specify the order. The indexing starts with 0. So, index: 0 means, this is going to be the first argument of the function, index: 1 means this is going to be the second argument of the function, etc.

Note that for some args types, char_buf and char_iovec, there are additional fields named returnCopy and sizeArgIndex available:

• returnCopy indicates that the corresponding argument should be read later (when the kretprobe for the symbol is triggered) because it might not be populated when the kprobe is triggered at the entrance of the function. For example, a buffer supplied to read(2) won’t have content until kretprobe is triggered.
• sizeArgIndex indicates the (1-based, see warning below) index of the arguments that represents the size of the char_buf or iovec. For example, for write(2), the third argument, size_t count is the number of char element that we can read from the const void *buf pointer from the second argument. Similarly, if we would like to capture the __x64_sys_writev(long, iovec *, vlen) syscall, then iovec has a size of vlen, which is going to be the third argument.

These flags can be combined, see the example below.

- call: "sys_write"
  syscall: true
  args:
  - index: 0
    type: "int"
  - index: 1
    type: "char_buf"
    returnCopy: true
    sizeArgIndex: 3
  - index: 2
    type: "size_t"

Note that you can specify which arguments you would like to print from a specific syscall. For example if you don’t care about the file descriptor, which is the first int argument with index: 0 and just want the char_buf, what is written, then you can leave this section out and just define:

args:
- index: 1
  type: "char_buf"
  returnCopy: true
  sizeArgIndex: 3
- index: 2
  type: "size_t"

This tells the printer to skip printing the int arg because it’s not useful.

For char_buf type up to the 4096 bytes are stored. Data with bigger size are cut and returned as truncated bytes.

You can specify maxData flag for char_buf type to read maximum possible data (currently 327360 bytes), like:

args:
- index: 1
  type: "char_buf"
  maxData: true
  sizeArgIndex: 3
- index: 2
  type: "size_t"

This field is only used for char_buff data. When this value is false (default), the bpf program will fetch at most 4096 bytes. In later kernels (>=5.4) tetragon supports fetching up to 327360 bytes if this flag is turned on.

The maxData flag does not work with returnCopy flag at the moment, so it’s usable only for syscalls/functions that do not require return probe to read the data.

Return values

A TracingPolicy spec can specify that the return value should be reported in the tracing output. To do this, the return parameter of the call needs to be set to true, and the returnArg parameter needs to be set to specify the type of the return argument. For example:

- call: "sk_alloc"
  syscall: false
  return: true
  args:
  - index: 1
    type: int
    label: "family"
  returnArg:
    type: sock

In this case, the sk_alloc hook is specified to return a value of type sock (a pointer to a struct sock). Whenever the sk_alloc hook is hit, not only will it report the family parameter in index 1, it will also report the socket that was created.

Return values for socket tracking

A unique feature of a sock being returned from a hook such as sk_alloc is that the socket it refers to can be tracked. Most networking hooks in the network stack are run in a context that is not that of the process that owns the socket for which the actions relate; this is because networking happens asynchronously and not entirely in-line with the process. The sk_alloc hook does, however, occur in the context of the process, such that the task, the PID, and the TGID are of the process that requested that the socket was created.

Specifying socket tracking tells Tetragon to store a mapping between the socket and the process’ PID and TGID; and to use that mapping when it sees the socket in a sock argument in another hook to replace the PID and TGID of the context with the process that actually owns the socket. This can be done by adding a returnArgAction to the call. Available actions are TrackSock and UntrackSock. See TrackSock and UntrackSock.

- call: "sk_alloc"
  syscall: false
  return: true
  args:
  - index: 1
    type: int
    label: "family"
  returnArg:
    type: sock
  returnArgAction: TrackSock

Socket tracking is only available on kernels >=5.3.

Lists

It’s possible to define list of functions and use it in the kprobe’s call field.

Following example traces all sys_dup[23] syscalls.

spec:
  lists:
  - name: "dups"
    type: "syscalls"
    values:
    - "sys_dup"
    - "sys_dup2"
    - "sys_dup3"
  kprobes:
  - call: "list:dups"

It is basically a shortcut for following policy:

spec:
  kprobes:
  - call: "sys_dup"
    syscall: true
  - call: "sys_dup2"
    syscall: true
  - call: "sys_dup3"
    syscall: true

As shown in subsequent examples, its main benefit is allowing a single definition for calls that have the same filters.

The list is defined under lists field with arbitrary values for name and values fields.

spec:
  lists:
  - name: "dups"
    type: "syscalls"
    values:
    - "sys_dup"
    - "sys_dup2"
    - "sys_dup3"
    ...

It’s possible to define multiple lists.

spec:
  lists:
  - name: "dups"
    type: "syscalls"
    values:
    - "sys_dup"
    - "sys_dup2"
    - "sys_dup3"
    name: "another"
    - "sys_open"
    - "sys_close"

Syscalls specified with sys_ prefix are translated to their 64 bit equivalent function names.

It’s possible to specify a syscall for an alternative ABI by using the ABI name as a prefix. For example:

spec:
  lists:
  - name: "dups"
    type: "syscalls"
    values:
    - "sys_dup"
    - "i386/sys_dup"
    name: "another"
    - "sys_open"
    - "sys_close"

Specific list can be referenced in kprobe’s call field with "list:NAME" value.

spec:
  lists:
  - name: "dups"
  ...

  kprobes:
  - call: "list:dups"

The kprobe definition creates a kprobe for each item in the list and shares the rest of the config specified for kprobe.

List can also specify type field that implies extra checks on the values (like for syscall type) or denote that the list is generated automatically (see below). User must specify syscall type for list with syscall functions. Also syscall functions can’t be mixed with regular functions in the list.

The additional selector configuration is shared with all functions in the list. In following example we create 3 kprobes that share the same pid filter.

spec:
  lists:
  - name: "dups"
    type: "syscalls"
    values:
    - "sys_dup"
    - "sys_dup2"
    - "sys_dup3"
  kprobes:
  - call: "list:dups"
    selectors:
    - matchPIDs:
      - operator: In
        followForks: true
        isNamespacePID: false
        values:
        - 12345

It’s possible to use argument filter together with the list.

It’s important to understand that the argument will be retrieved by using the specified argument type for all the functions in the list.

Following example adds argument filter for first argument on all functions in dups list to match value 9999.

spec:
  lists:
  - name: "dups"
    type: "syscalls"
    values:
    - "sys_dup"
    - "sys_dup2"
    - "sys_dup3"
  kprobes:
  - call: "list:dups"
    args:
    - index: 0
      type: int
    selectors:
    - matchArgs:
      - index: 0
        operator: "Equal"
        values:
        - 9999

There are two additional special types of generated lists.

The generated_syscalls type of list that generates list with all possible syscalls on the system.

Following example traces all syscalls for /usr/bin/kill binary.

spec:
  lists:
  - name: "all-syscalls"
    type: "generated_syscalls"
  kprobes:
  - call: "list:all-syscalls"
    selectors:
    - matchBinaries:
      - operator: "In"
        values:
        - "/usr/bin/kill"

The generated_ftrace type of list that generates functions from ftrace available_filter_functions file with specified filter. The filter is specified with pattern field and expects regular expression.

Following example traces all kernel ksys_* functions for /usr/bin/kill binary.

spec:
  lists:
  - name: "ksys"
    type: "generated_ftrace"
    pattern: "^ksys_*"
  kprobes:
  - call: "list:ksys"
    selectors:
    - matchBinaries:
      - operator: "In"
        values:
        - "/usr/bin/kill"

Note that if syscall list is used in selector with InMap operator, the argument type needs to be syscall64, like.

spec:
  lists:
  - name: "dups"
    type: "syscalls"
    values:
    - "sys_dup"
    - "i386/sys_dup"
  tracepoints:
  - subsystem: "raw_syscalls"
    event: "sys_enter"
    args:
    - index: 4
      type: "syscall64"
    selectors:
    - matchArgs:
      - index: 0
        operator: "InMap"
        values:
        - "list:dups"

4.2.3 - Options

It’s possible to pass options through spec file as an array of name and value pairs:

spec:
  options:
    - name: "option-1"
      value: "True"
    - name: "option-2"
      value: "10"

Options array is passed and processed by each hook used in the spec file that supports options. At the moment it’s availabe for kprobe and uprobe hooks.

• Kprobe Options: options for kprobe hooks.

Kprobe options

• disable-kprobe-multi: disable kprobe multi link

disable-kprobe-multi

This option disables kprobe multi link interface for all the kprobes defined in the spec file. If enabled, all the defined kprobes will be atached through standard kprobe interface. It stays enabled for another spec file without this option.

It takes boolean as value, by default it’s false.

Example:

  options:
    - name: "disable-kprobe-multi"
      value: "1"

• Uprobe Options: options for uprobe hooks.

Uprobe options

• disable-uprobe-multi: disable uprobe multi link

disable-uprobe-multi

This option disables uprobe multi link interface for all the uprobes defined in the spec file. If enabled, all the defined uprobes will be atached through standard uprobe interface. It stays enabled for another spec file without this option.

It takes boolean as value, by default it’s false.

Example:

  options:
    - name: "disable-uprobe-multi"
      value: "1"

4.2.4 - Selectors

Selectors are a way to perform in-kernel BPF filtering on the events to export, or on the events on which to apply an action.

A TracingPolicy can contain from 0 to 5 selectors. A selector is composed of 1 or more filters. The available filters are the following:

• matchArgs: filter on the value of arguments.
• matchReturnArgs: filter on the return value.
• matchPIDs: filter on PID.
• matchBinaries: filter on binary path.
• matchNamespaces: filter on Linux namespaces.
• matchCapabilities: filter on Linux capabilities.
• matchNamespaceChanges: filter on Linux namespaces changes.
• matchCapabilityChanges: filter on Linux capabilities changes.
• matchActions: apply an action on selector matching.
• matchReturnActions: apply an action on return selector matching.

Arguments filter

Arguments filters can be specified under the matchArgs field and provide filtering based on the value of the function’s argument.

In the next example, a selector is defined with a matchArgs filter that tells the BPF code to process only the function call for which the second argument, index equal to 1, concerns the file under the path /etc/passwd or /etc/shadow. It’s using the operator Equal to match against the value of the argument.

Note that conveniently, we can match against a path directly when the argument is of type file.

selectors:
- matchArgs:
  - index: 1
    operator: "Equal"
    values:
    - "/etc/passwd"
    - "/etc/shadow"

The available operators for matchArgs are:

• Equal
• NotEqual
• Prefix
• Postfix
• Mask

Further examples

In the previous example, we used the operator Equal, but we can also use the Prefix operator and match against all files under /etc with:

selectors:
- matchArgs:
  - index: 1
    operator: "Prefix"
    values:
    - "/etc"

In this situation, an event will be created every time a process tries to access a file under /etc.

Although it makes less sense, you can also match over the first argument, to only detect events that will use the file descriptor 4, which is usually the first that come afters stdin, stdout and stderr in process. And combine that with the previous example.

- matchArgs:
  - index: 0
    operator: "Equal"
    values:
    - "3"
  - index: 1
    operator: "Prefix"
    values:
    - "/etc"

Return args filter

Arguments filters can be specified under the returnMatchArgs field and provide filtering based on the value of the function return value. It allows you to filter on the return value, thus success, error or value returned by a kernel call.

matchReturnArgs:
- operator: "NotEqual"
  values:
  - 0

The available operators for matchReturnArgs are:

• Equal
• NotEqual
• Prefix
• Postfix

A use case for this would be to detect the failed access to certain files, like /etc/shadow. Doing cat /etc/shadow will use a openat syscall that will returns -1 for a failed attempt with an unprivileged user.

PIDs filter

PIDs filters can be specified under the matchPIDs field and provide filtering based on the value of host pid of the process. For example, the following matchPIDs filter tells the BPF code that observe only hooks for which the host PID is equal to either pid1 or pid2 or pid3:

- matchPIDs:
  - operator: "In"
    followForks: true
    values:
    - "pid1"
    - "pid2"
    - "pid3"

The available operators for matchPIDs are:

• In
• NotIn

Further examples

Another example can be to collect all processes not associated with a container’s init PID, which is equal to 1. In this way, we are able to detect if there was a kubectl exec performed inside a container because processes created by kubectl exec are not children of PID 1.

- matchPIDs:
  - operator: NotIn
    followForks: false
    isNamespacePID: true
    values:
    - 1

Binaries filter

Binary filters can be specified under the matchBinaries field and provide filtering based on the value of a certain binary name. For example, the following matchBinaries selector tells the BPF code to process only system calls and kernel functions that are coming from cat or tail.

- matchBinaries:
  - operator: "In"
    values:
    - "/usr/bin/cat"
    - "/usr/bin/tail"

The available operators for matchBinaries are:

• In
• NotIn
• Prefix
• NotPrefix
• Postfix
• NotPostfix

The values field has to be a map of strings. The default behaviour is followForks: true, so all the child processes are followed.

Follow children

the matchBinaries filter can be configured to also apply to children of matching processes. To do this, set followChildren to true. For example:

- matchBinaries:
  - operator: "In"
    values:
    - "/usr/sbin/sshd"
    followChildren: true

There are a number of limitations when using followChildren:

• Children created before the policy was installed will not be matched
• The number of matchBinaries sections with followChildren: true cannot exceed 64.
• Operators other than In are not supported.

Further examples

One example can be to monitor all the sys_write system calls which are coming from the /usr/sbin/sshd binary and its child processes and writing to stdin/stdout/stderr.

This is how we can monitor what was written to the console by different users during different ssh sessions. The matchBinaries selector in this case is the following:

- matchBinaries:
  - operator: "In"
    values:
    - "/usr/sbin/sshd"

while the whole kprobe call is the following:

- call: "sys_write"
  syscall: true
  args:
  - index: 0
    type: "int"
  - index: 1
    type: "char_buf"
    sizeArgIndex: 3
  - index: 2
    type: "size_t"
  selectors:
  # match to /sbin/sshd
  - matchBinaries:
    - operator: "In"
      values:
      - "/usr/sbin/sshd"
  # match to stdin/stdout/stderr
    matchArgs:
    - index: 0
      operator: "Equal"
      values:
      - "1"
      - "2"
      - "3"

Namespaces filter

Namespaces filters can be specified under the matchNamespaces field and provide filtering of calls based on Linux namespace. You can specify the namespace inode or use the special host_ns keyword, see the example and description for more information.

An example syntax is:

- matchNamespaces:
  - namespace: Pid
    operator: In
    values:
    - "4026531836"
    - "4026531835"

This will match if: [Pid namespace is 4026531836] OR [Pid namespace is 4026531835]

• namespace can be: Uts, Ipc, Mnt, Pid, PidForChildren, Net, Cgroup, or User. Time and TimeForChildren are also available in Linux >= 5.6.
• operator can be In or NotIn
• values can be raw numeric values (i.e. obtained from lsns) or "host_ns" which will automatically be translated to the appropriate value.

Limitations

1. We can have up to 4 values. These can be both numeric and host_ns inside a single namespace.
2. We can have up to 4 namespace values under matchNamespaces in Linux kernel < 5.3. In Linux >= 5.3 we can have up to 10 values (i.e. the maximum number of namespaces that modern kernels provide).

Further examples

We can have multiple namespace filters:

selectors:
- matchNamespaces:
  - namespace: Pid
    operator: In
    values:
    - "4026531836"
    - "4026531835"
  - namespace: Mnt
    operator: In
    values:
    - "4026531833"
    - "4026531834"

This will match if: ([Pid namespace is 4026531836] OR [Pid namespace is 4026531835]) AND ([Mnt namespace is 4026531833] OR [Mnt namespace is 4026531834])

Use cases examples

Generate a kprobe event if /etc/shadow was opened by /bin/cat which either had host Net or Mnt namespace access

apiVersion: cilium.io/v1alpha1
kind: TracingPolicy
metadata:
  name: "example_ns_1"
spec:
  kprobes:
    - call: "fd_install"
      syscall: false
      args:
        - index: 0
          type: int
        - index: 1
          type: "file"
      selectors:
        - matchBinaries:
          - operator: "In"
            values:
            - "/bin/cat"
          matchArgs:
          - index: 1
            operator: "Equal"
            values:
            - "/etc/shadow"
          matchNamespaces:
          - namespace: Mnt
            operator: In
            values:
            - "host_ns"
        - matchBinaries:
          - operator: "In"
            values:
            - "/bin/cat"
          matchArgs:
          - index: 1
            operator: "Equal"
            values:
            - "/etc/shadow"
          matchNamespaces:
          - namespace: Net
            operator: In
            values:
            - "host_ns"

This example has 2 selectors. Note that each selector starts with -.

Selector 1:

        - matchBinaries:
          - operator: "In"
            values:
            - "/bin/cat"
          matchArgs:
          - index: 1
            operator: "Equal"
            values:
            - "/etc/shadow"
          matchNamespaces:
          - namespace: Mnt
            operator: In
            values:
            - "host_ns"

Selector 2:

        - matchBinaries:
          - operator: "In"
            values:
            - "/bin/cat"
          matchArgs:
          - index: 1
            operator: "Equal"
            values:
            - "/etc/shadow"
          matchNamespaces:
          - namespace: Net
            operator: In
            values:
            - "host_ns"

We have [Selector1 OR Selector2]. Inside each selector we have filters. Both selectors have 3 filters (i.e. matchBinaries, matchArgs, and matchNamespaces) with different arguments. Adding a - in the beginning of a filter will result in a new selector.

So the previous CRD will match if:

[binary == /bin/cat AND arg1 == /etc/shadow AND MntNs == host] OR [binary == /bin/cat AND arg1 == /etc/shadow AND NetNs is host]

We can modify the previous example as follows:

Generate a kprobe event if /etc/shadow was opened by /bin/cat which has host Net and Mnt namespace access

apiVersion: cilium.io/v1alpha1
kind: TracingPolicy
metadata:
  name: "example_ns_2"
spec:
  kprobes:
    - call: "fd_install"
      syscall: false
      args:
        - index: 0
          type: int
        - index: 1
          type: "file"
      selectors:
        - matchBinaries:
          - operator: "In"
            values:
            - "/bin/cat"
          matchArgs:
          - index: 1
            operator: "Equal"
            values:
            - "/etc/shadow"
          matchNamespaces:
          - namespace: Mnt
            operator: In
            values:
            - "host_ns"
          - namespace: Net
            operator: In
            values:
            - "host_ns"

Here we have a single selector. This CRD will match if:

[binary == /bin/cat AND arg1 == /etc/shadow AND (MntNs == host AND NetNs == host) ]

Capabilities filter

Capabilities filters can be specified under the matchCapabilities field and provide filtering of calls based on Linux capabilities in the specific sets.

An example syntax is:

- matchCapabilities:
  - type: Effective
    operator: In
    values:
    - "CAP_CHOWN"
    - "CAP_NET_RAW"

This will match if: [Effective capabilities contain CAP_CHOWN] OR [Effective capabilities contain CAP_NET_RAW]

• type can be: Effective, Inheritable, or Permitted.
• operator can be In or NotIn
• values can be any supported capability. A list of all supported capabilities can be found in /usr/include/linux/capability.h.

Limitations

1. There is no limit in the number of capabilities listed under values.
2. Only one type field can be specified under matchCapabilities.

Namespace changes filter

Namespace changes filter can be specified under the matchNamespaceChanges field and provide filtering based on calls that are changing Linux namespaces. This filter can be useful to track execution of code in a new namespace or even container escapes that change their namespaces.

For instance, if an unprivileged process creates a new user namespace, it gains full privileges within that namespace. This grants the process the ability to perform some privileged operations within the context of this new namespace that would otherwise only be available to privileged root user. As a result, such filter is useful to track namespace creation, which can be abused by untrusted processes.

To keep track of the changes, when a process_exec happens, the namespaces of the process are recorded and these are compared with the current namespaces on the event with a matchNamespaceChanges filter.

matchNamespaceChanges:
- operator: In
  values:
  - "Mnt"

The unshare command, or executing in the host namespace using nsenter can be used to test this feature. See a demonstration example of this feature.

Capability changes filter

Capability changes filter can be specified under the matchCapabilityChanges field and provide filtering based on calls that are changing Linux capabilities.

To keep track of the changes, when a process_exec happens, the capabilities of the process are recorded and these are compared with the current capabilities on the event with a matchCapabilityChanges filter.

matchCapabilityChanges:
- type: Effective
  operator: In
  isNamespaceCapability: false
  values:
  - "CAP_SETUID"

See a demonstration example of this feature.

Actions filter

Actions filters are a list of actions that execute when an appropriate selector matches. They are defined under matchActions and currently, the following action types are supported:

• Sigkill action
• Signal action
• Override action
• FollowFD action
• UnfollowFD action
• CopyFD action
• GetUrl action
• DnsLookup action
• Post action
• NoPost action
• TrackSock action
• UntrackSock action
• Notify Enforcer action

Sigkill action

Sigkill action terminates synchronously the process that made the call that matches the appropriate selectors from the kernel. In the example below, every sys_write system call with a PID not equal to 1 or 0 attempting to write to /etc/passwd will be terminated. Indeed when using kubectl exec, a new process is spawned in the container PID namespace and is not a child of PID 1.

- call: "sys_write"
  syscall: true
  args:
  - index: 0
    type: "fd"
  - index: 1
    type: "char_buf"
    sizeArgIndex: 3
  - index: 2
    type: "size_t"
  selectors:
  - matchPIDs:
    - operator: NotIn
      followForks: true
      isNamespacePID: true
      values:
      - 0
      - 1
    matchArgs:
    - index: 0
      operator: "Prefix"
      values:
      - "/etc/passwd"
    matchActions:
    - action: Sigkill

Signal action

Signal action sends specified signal to current process. The signal number is specified with argSig value.

Following example is equivalent to the Sigkill action example above. The difference is to use the signal action with SIGKILL(9) signal.

- call: "sys_write"
  syscall: true
  args:
  - index: 0
    type: "fd"
  - index: 1
    type: "char_buf"
    sizeArgIndex: 3
  - index: 2
    type: "size_t"
  selectors:
  - matchPIDs:
    - operator: NotIn
      followForks: true
      isNamespacePID: true
      values:
      - 0
      - 1
    matchArgs:
    - index: 0
      operator: "Prefix"
      values:
      - "/etc/passwd"
    matchActions:
    - action: Signal
      argSig: 9

Override action

Override action allows to modify the return value of call. While Sigkill will terminate the entire process responsible for making the call, Override will run in place of the original kprobed function and return the value specified in the argError field. It’s then up to the code path or the user space process handling the returned value to whether stop or proceed with the execution.

For example, you can create a TracingPolicy that intercepts sys_symlinkat and will make it return -1 every time the first argument is equal to the string /etc/passwd:

kprobes:
- call: "sys_symlinkat"
  syscall: true
  args:
  - index: 0
    type: "string"
  - index: 1
    type: "int"
  - index: 2
    type: "string"
  selectors:
  - matchArgs:
    - index: 0
      operator: "Equal"
      values:
      - "/etc/passwd\0"
    matchActions:
    - action: Override
      argError: -1

FollowFD action

The FollowFD action allows to create a mapping using a BPF map between file descriptors and filenames. After its creation, the mapping can be maintained through UnfollowFD and CopyFD actions. Note that proper maintenance of the mapping is up to the tracing policy writer.

FollowFD is typically used at hook points where a file descriptor and its associated filename appear together. The kernel function fd_install is a good example.

The fd_install kernel function is called each time a file descriptor must be installed into the file descriptor table of a process, typically referenced within system calls like open or openat. It is a good place for tracking file descriptor and filename matching.

Let’s take a look at the following example:

- call: "fd_install"
  syscall: false
  args:
  - index: 0
    type: int
  - index: 1
    type: "file"
  selectors:
  - matchPIDs:
      # [...]
    matchArgs:
      # [...]
    matchActions:
    - action: FollowFD
      argFd: 0
      argName: 1

This action uses the dedicated argFd and argName fields to get respectively the index of the file descriptor argument and the index of the name argument in the call.

While the mapping between the file descriptor and filename remains in place (that is, between FollowFD and UnfollowFD for the same file descriptor) tracing policies may refer to filenames instead of file descriptors. This offers greater convenience and allows more functionality to reside inside the kernel, thereby reducing overhead.

For instance, assume that you want to prevent writes into file /etc/passwd. The system call sys_write only receives a file descriptor, not a filename, as argument. Yet with a bracketing pair of FollowFD and UnfollowFD actions in place the tracing policy that hooks into sys_write can nevertheless refer to the filename /etc/passwd, if it also marks the relevant argument as of type fd.

The following example combines actions FollowFD and UnfollowFD as well as an argument of type fd to such effect:

kprobes:
- call: "fd_install"
  syscall: false
  args:
  - index: 0
    type: int
  - index: 1
    type: "file"
  selectors:
  - matchArgs:
    - index: 1
      operator: "Equal"
      values:
      - "/tmp/passwd"
    matchActions:
    - action: FollowFD
      argFd: 0
      argName: 1
- call: "sys_write"
  syscall: true
  args:
  - index: 0
    type: "fd"
  - index: 1
    type: "char_buf"
    sizeArgIndex: 3
  - index: 2
    type: "size_t"
  selectors:
  - matchArgs:
    - index: 0
      operator: "Equal"
      values:
      - "/tmp/passwd"
    matchActions:
    - action: Sigkill
- call: "sys_close"
  syscall: true
  args:
  - index: 0
     type: "int"
  selectors:
  - matchActions:
    - action: UnfollowFD
      argFd: 0
      argName: 0