chancez/tetragon

eBPF-based Security Observability and Runtime Enforcement

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

Tetragon detects and is able to respond in real time to security-significant events, such as

• Process execution events
• Changes to privileges and capabilities
• 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 real-time security and observability tool. What this means is Tetragon applies policy and filtering directly in eBPF. In the security context, this enables stopping an operation from occurring, instead of observing an operation and reacting to it (e.g., detected malicious behavior) after the fact. In the react case, an attacker may have already manipulated the critical data, stolen secrets, or otherwise compromised the machine. By applying policy inline in eBPF, malicious operations are stopped before they occur.

For an observability use case, applying filters directly in the kernel drastically reduces observation overhead. By avoiding expensive context switching and wakeups, 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.

Local Development

For getting started with local development, you can refer to the Development Guide.

Quickstart Guide

This Quickstart guide uses a Kind cluster and a helm-based installation to provide a simple way to get a hands on experience with Tetragon and the generated events. These events include monitoring process execution, network sockets, and file access to see what binaries are executing and making network connections or writing to sensitive files.

In this scenario, we are going to install a demo application,

• observe all process execution happening inside a Kubernetes workload
• detect file access and writes
• observe network connections that a Kubernetes workload is making
• detect privileged processes inside a Kubernetes workload

While, we use a Kubernetes Kind cluster in this guide, users can also apply the same concepts in other Kubernetes platforms, bare-metal, or VM environments.

Requirements

The base kernel should support BTF or the BTF file should be placed where Tetragon can read it.

For reference, the examples below use this Vagrantfile and we created our Kind cluster using the defaults options.

Create a cluster

Create a Kubernetes cluster using Kind or GKE.

Kind

Run the following command to create the Kubernetes cluster:

kind create cluster

GKE

Run the following command to create a GKE cluster:

export NAME="$(whoami)-$RANDOM"
gcloud container clusters create "${NAME}" \
  --zone us-west2-a \
  --release-channel rapid \
  --num-nodes 1 \

Deploy Tetragon

To install and deploy Tetragon, run the following from the git repository. Note if running the Vagrantfile above, the repository is synced with the VM.

helm install tetragon -n kube-system ./install/kubernetes/
kubectl rollout status -n kube-system ds/tetragon -w

By default kube-system pods are filtered for the examples below we use the demo deployment from Cilium to generate events.

Deploy Demo Application

Once Tetragon is installed, you can use our Demo Application to explore the Security Observability Events:

kubectl create -f https://raw.githubusercontent.com/cilium/cilium/v1.11/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 until they satisfy all the dependencies:

kubectl get pods
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

Explore Security Observability Events

After Tetragon and the Demo Application is up and running you can examine the security and observability events produced by Tetragon in different ways.

Raw JSON events

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

kubectl logs -n kube-system ds/tetragon -c export-stdout -f

NOTE: If you're running more than one tetragon pod then the command above will only print the logs from one of those pods. To print out the logs on all tetragon pods, you will need to use a filter/selector such as -l app.kubernetes.io/name=tetragon:

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.

Tetragon CLI

A second way is to pretty print the events using the Tetragon CLI. The tool also allows filtering by process, pod, and other fields.

You can download and install it by the following command:

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

(see https://github.com/cilium/tetragon/releases/tag/tetragon-cli for supported GOOS/GOARCH binary releases)

To start printing events run:

kubectl logs -n kube-system ds/tetragon -c export-stdout -f | tetragon observe

Tetragon is able to observe several events, here we provide a few small samples that can be used as a starting point:

Process Execution

This first use case is monitoring process execution, which can be observed with the Tetragon process_exec and process_exit JSON events. These events contain the full lifecycle of processes, from fork/exec to exit, including metadata such as:

• Binary name: Defines the name of an executable file
• Parent process: Helps to identify process execution anomalies (e.g., if a nodejs app forks a shell, this is suspicious)
• Command-line argument: Defines the program runtime behavior
• Current working directory: Helps to identify hidden malware execution from a temporary folder, which is a common pattern used in malwares
• Kubernetes metadata: Contains pods, labels, and Kubernetes namespaces, which are critical to identify service owners, particularly in a multitenant environments
• exec_id: A unique process identifier that correlates all recorded activity of a process

As a first step, let's start monitoring the events from the xwing pod:

kubectl logs -n kube-system ds/tetragon -c export-stdout -f | tetragon observe --namespace default --pod xwing

Then in another terminal, let's kubectl exec into the xwing pod and execute some example commands:

kubectl exec -it xwing -- /bin/bash
whoami

If you observe, the output in the first terminal should be:

🚀 process default/xwing /bin/bash
🚀 process default/xwing /usr/bin/whoami
💥 exit    default/xwing /usr/bin/whoami 0

Here you can see the binary names along with its arguments, the pod info, and return codes. For a compact one line view of the events.

For more details use the raw JSON events to get detailed information, you can stop the Tetragon CLI by Crl-C and parse the tetragon.log file by executing:

kubectl logs -n kube-system ds/tetragon -c export-stdout -f | jq 'select(.process_exec.process.pod.name=="xwing" or .process_exit.process.pod.name=="xwing")'

Example process_exec and process_exit events can be:

Process Exec Event

{
  "process_exec": {
    "process": {
      "exec_id": "a2luZC1jb250cm9sLXBsYW5lOjExNDI4NjE1NjM2OTAxOjUxNTgz",
      "pid": 51583,
      "uid": 0,
      "cwd": "/",
      "binary": "/usr/bin/whoami",
      "arguments": "--version",
      "flags": "execve rootcwd clone",
      "start_time": "2022-05-11T12:54:45.615Z",
      "auid": 4294967295,
      "pod": {
        "namespace": "default",
        "name": "xwing",
        "container": {
          "id": "containerd://1fb931d2f6e5e4cfdbaf30fdb8e2fdd81320bdb3047ded50120a4f82838209ce",
          "name": "spaceship",
          "image": {
            "id": "docker.io/tgraf/netperf@sha256:8e86f744bfea165fd4ce68caa05abc96500f40130b857773186401926af7e9e6",
            "name": "docker.io/tgraf/netperf:latest"
          },
          "start_time": "2022-05-11T10:07:33Z",
          "pid": 50
        }
      },
      "docker": "1fb931d2f6e5e4cfdbaf30fdb8e2fdd",
      "parent_exec_id": "a2luZC1jb250cm9sLXBsYW5lOjkwNzkyMjU2MjMyNjk6NDM4NzI=",
      "refcnt": 1
    },
    "parent": {
      "exec_id": "a2luZC1jb250cm9sLXBsYW5lOjkwNzkyMjU2MjMyNjk6NDM4NzI=",
      "pid": 43872,
      "uid": 0,
      "cwd": "/",
      "binary": "/bin/bash",
      "flags": "execve rootcwd clone",
      "start_time": "2022-05-11T12:15:36.225Z",
      "auid": 4294967295,
      "pod": {
        "namespace": "default",
        "name": "xwing",
        "container": {
          "id": "containerd://1fb931d2f6e5e4cfdbaf30fdb8e2fdd81320bdb3047ded50120a4f82838209ce",
          "name": "spaceship",
          "image": {
            "id": "docker.io/tgraf/netperf@sha256:8e86f744bfea165fd4ce68caa05abc96500f40130b857773186401926af7e9e6",
            "name": "docker.io/tgraf/netperf:latest"
          },
          "start_time": "2022-05-11T10:07:33Z",
          "pid": 43
        }
      },
      "docker": "1fb931d2f6e5e4cfdbaf30fdb8e2fdd",
      "parent_exec_id": "a2luZC1jb250cm9sLXBsYW5lOjkwNzkxODU5NTMzOTk6NDM4NjE=",
      "refcnt": 1
    }
  },
  "node_name": "kind-control-plane",
  "time": "2022-05-11T12:54:45.615Z"
}

Process Exit Event

{
  "process_exit": {
    "process": {
      "exec_id": "a2luZC1jb250cm9sLXBsYW5lOjExNDI4NjE1NjM2OTAxOjUxNTgz",
      "pid": 51583,
      "uid": 0,
      "cwd": "/",
      "binary": "/usr/bin/whoami",
      "arguments": "--version",
      "flags": "execve rootcwd clone",
      "start_time": "2022-05-11T12:54:45.615Z",
      "auid": 4294967295,
      "pod": {
        "namespace": "default",
        "name": "xwing",
        "container": {
          "id": "containerd://1fb931d2f6e5e4cfdbaf30fdb8e2fdd81320bdb3047ded50120a4f82838209ce",
          "name": "spaceship",
          "image": {
            "id": "docker.io/tgraf/netperf@sha256:8e86f744bfea165fd4ce68caa05abc96500f40130b857773186401926af7e9e6",
            "name": "docker.io/tgraf/netperf:latest"
          },
          "start_time": "2022-05-11T10:07:33Z",
          "pid": 50
        }
      },
      "docker": "1fb931d2f6e5e4cfdbaf30fdb8e2fdd",
      "parent_exec_id": "a2luZC1jb250cm9sLXBsYW5lOjkwNzkyMjU2MjMyNjk6NDM4NzI="
    },
    "parent": {
      "exec_id": "a2luZC1jb250cm9sLXBsYW5lOjkwNzkyMjU2MjMyNjk6NDM4NzI=",
      "pid": 43872,
      "uid": 0,
      "cwd": "/",
      "binary": "/bin/bash",
      "flags": "execve rootcwd clone",
      "start_time": "2022-05-11T12:15:36.225Z",
      "auid": 4294967295,
      "pod": {
        "namespace": "default",
        "name": "xwing",
        "container": {
          "id": "containerd://1fb931d2f6e5e4cfdbaf30fdb8e2fdd81320bdb3047ded50120a4f82838209ce",
          "name": "spaceship",
          "image": {
            "id": "docker.io/tgraf/netperf@sha256:8e86f744bfea165fd4ce68caa05abc96500f40130b857773186401926af7e9e6",
            "name": "docker.io/tgraf/netperf:latest"
          },
          "start_time": "2022-05-11T10:07:33Z",
          "pid": 43
        }
      },
      "docker": "1fb931d2f6e5e4cfdbaf30fdb8e2fdd",
      "parent_exec_id": "a2luZC1jb250cm9sLXBsYW5lOjkwNzkxODU5NTMzOTk6NDM4NjE="
    }
  },
  "node_name": "kind-control-plane",
  "time": "2022-05-11T12:54:45.616Z"
}

For the rest of the use cases we will use the Tetragon CLI to give the output.

File Access

The second use case is file access, which can be observed with the Tetragon process_kprobe JSON events. By using kprobe hook points, these events are able to observe arbitrary kernel calls and file descriptors in the Linux kernel, giving you the ability to monitor every file a process opens, reads, writes, and closes throughout its lifecycle. To be able to observe arbitrary kernel calls, Tetragon can be extended with TracingPolicies.

TracingPolicy is a user-configurable Kubernetes custom resource definition (CRD) that allows users to trace arbitrary events in the kernel and define actions to take on a match. For bare metal or VM use cases without Kubernetes a YAML configuration file may be used.

In this example, we can monitor if a process inside a Kubernetes workload performs an open, close, read or write in the /etc/ directory. The policy may further specify additional dirs or specific files if needed.

As a first step, let's apply the following TracingPolicy:

kubectl apply -f ./crds/examples/sys_write_follow_fd_prefix.yaml

As a second step, let's start monitoring the events from the xwing pod:

kubectl logs -n kube-system ds/tetragon -c export-stdout -f | tetragon observe --namespace default --pod xwing

In another terminal, kubectl exec into the xwing pod:

kubectl exec -it xwing -- /bin/bash

and edit the /etc/passwd file:

vi /etc/passwd

If you observe, the output in the first terminal should be:

🚀 process default/xwing /usr/bin/vi /etc/passwd
📬 open    default/xwing /usr/bin/vi /etc/passwd
📚 read    default/xwing /usr/bin/vi /etc/passwd 1269 bytes
📪 close   default/xwing /usr/bin/vi /etc/passwd
📬 open    default/xwing /usr/bin/vi /etc/passwd
📝 write   default/xwing /usr/bin/vi /etc/passwd 1277 bytes
💥 exit    default/xwing /usr/bin/vi /etc/passwd 0

Note, that open and close are only generated for '/etc/' files because of eBPF in kernel filtering. The default CRD additionally filters events associated with the pod init process to filter init noise from pod start.

Similar to the previous example, reviewing the JSON events provides additional data on the event. In addition to the Kubernetes identity and process metadata from exec events, process_kprobe events contain the arguments of the observed system call. In the above case they are

• path: the observed file path
• bytes_arg: content of the observed file encoded in base64
• size_arg: size of the observed file in bytes

To disable the TracingPolicy run:

kubectl delete -f ./crds/examples/sys_write_follow_fd_prefix.yaml

Network Observability

To view TCP connect events apply the example TCP connect tracing policy.

kubectl apply -f ./crds/examples/tcp-connect.yaml

To start monitoring events in the xwing pod run the observer,

kubectl logs -n kube-system ds/tetragon -c export-stdout -f | tetragon observe --namespace default --pod xwing

In another terminal, start generate a TCP connection. Here we use curl.

kubectl exec -it xwing -- curl http://cilium.io

The output in the first terminal will capture the new connect and write,

🚀 process default/xwing /usr/bin/curl http://cilium.io
🔌 connect default/xwing /usr/bin/curl tcp 10.244.0.6:34965 -> 104.198.14.52:80
📤 sendmsg default/xwing /usr/bin/curl tcp 10.244.0.6:34965 -> 104.198.14.52:80 bytes 73
🧹 close   default/xwing /usr/bin/curl tcp 10.244.0.6:34965 -> 104.198.14.52:80
💥 exit    default/xwing /usr/bin/curl http://cilium.io 0

To disable the TracingPolicy run:

kubectl delete -f ./crds/examples/tcp-connect.yaml

Privileged Execution

Tetragon also provides the ability to check process capabilities and kernel namespaces.

As a first step let's enable visibility to capability and namespace changes via the configmap by setting enable-process-cred and enable-process-ns from false to true

kubectl edit cm -n kube-system tetragon-config
# change "enable-process-cred" from "false" to "true"
# change "enable-process-ns" from "false" to "true"
# then save and exit

Restart the Tetragon daemonset:

kubectl rollout restart -n kube-system ds/tetragon

As a second step, let's start monitoring the Security Observability events from the privileged test-pod workload:

kubectl logs -n kube-system ds/tetragon -c export-stdout -f | tetragon observe --namespace default --pod test-pod

In another terminal let's apply the privileged PodSpec:

kubectl apply -f ./testdata/specs/testpod.yaml

If you observe the output in the first terminal, you can see the container start with CAP_SYS_ADMIN:

🚀 process default/test-pod /bin/sleep 365d                🛑 CAP_SYS_ADMIN
🚀 process default/test-pod /usr/bin/jq -r .bundle         🛑 CAP_SYS_ADMIN
🚀 process default/test-pod /usr/bin/cp /kind/product_name /kind/product_uuid /run/containerd/io.containerd.runtime.v2.task/k8s.io/7c7e513cd4d506417bc9d97dd9af670d94d9e84161c8c8 fdc9fa3a678289a59/rootfs/ 🛑 CAP_SYS_ADMIN

BTF Requirement

Tetragon repository provides a Vagrantfile that can be use to install a vagrant box for running Tetragon with BTF requirement. Other VM solutions work as well and many common Linux distributions now ship with BTF and do not require any extra work. To check if BTF is enabled on your Linux system check for the BTF file in the standard location,

$ ls /sys/kernel/btf/

To run with vagrant we provide a standard VagrantFile with the required components enabled. Simply run,

 $ vagrant up
 $ vagrant ssh

This should be sufficient to create a Kind cluster and run Tetragon.

Community

Slack

Join the Tetragon Slack channel to chat with developers, maintainers, and other users. This is a good first stop to ask questions and share your experiences.