Agent skill

Analyzing Docker Container Forensics

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Investigate compromised Docker containers by analyzing images, layers, volumes, logs, and runtime artifacts to identify malicious activity and evidence.

Apache-2.0Auto-check: notesDevOps & Cloud

Install Analyzing Docker Container Forensics

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-docker-container-forensics -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills analyzing-docker-container-forensics --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/analyzing-docker-container-forensics .claude/skills/analyzing-docker-container-forensics && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
analyzing-docker-container-forensics
GitHub stars
34k
Token cost
~3.2k tokens
SKILL.md length
462 words
Files
4 (incl. scripts, references)
Skills in repo
637
Repo updated
First seen
Licence
Apache-2.0

At a glance

Investigate compromised Docker containers by analyzing images, layers, volumes, logs, and runtime artifacts to identify malicious activity and evidence.

  • Works in 5 steps: Preserve Container State and Evidence → Analyze Container Image Layers → Examine Docker Host Artifacts → …
  • Tasks that involve Containers
  • SKILL.md covers When to Use, Prerequisites, Workflow and Key Concepts, plus 3 more sections
  • Runs Python scripts from its folder; calls docker, python3 and trivy; reaches github.com and storage.googleapis.com

What it does

Analyzing Docker Container Forensics is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Investigate compromised Docker containers by analyzing images, layers, volumes, logs, and runtime artifacts to identify malicious activity and evidence.

Its SKILL.md is about 3.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/api-reference.md` and `scripts/agent.py`).

It sits in DevOps & Cloud, covering Containers. It works with Docker. The repository describes itself as: 817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io…. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Containers

Example prompts

  • “/analyzing-docker-container-forensics”

Requirements

  • Python 3
  • Docker

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Preserve Container State and Evidence
  2. Analyze Container Image Layers
  3. Examine Docker Host Artifacts
  4. Analyze Container File System Changes
  5. Scan for Vulnerabilities and Generate Report

What it can do on your machine

Read from SKILL.md and the folder at commit 54a7988. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • docker
    • python3
    • trivy
    • wget
    • curl

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • github.com
    • storage.googleapis.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Analyzing Docker Container Forensics loads about 3.2k tokens when it runs, and up to ~4k if it reads all its reference files. Until then it costs about 47 tokens; SKILL.md has 462 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~47
When it runs · the whole SKILL.md, loaded when a task matches
~3.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteRuns commands with sudoSKILL.md:87
    sudo dpkg -i dive_linux_amd64.deb

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from mukul975/Anthropic-Cybersecurity-Skills at commit 54a7988, republished under its Apache-2.0 licence (© mukul975). 462 words, ~3,222 tokens.

Download SKILL.mdSave it as .claude/skills/analyzing-docker-container-forensics/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
analyzing-docker-container-forensics
description
Investigate compromised Docker containers by analyzing images, layers, volumes, logs, and runtime artifacts to identify malicious activity and evidence.
domain
cybersecurity
subdomain
digital-forensics
tags
forensics, docker, container-forensics, container-security, image-analysis, runtime-investigation
version
1.0
author
mahipal
license
Apache-2.0
nist_csf
RS.AN-03, DE.AE-02, RS.MA-01
mitre_attack
T1610, T1611, T1613, T1612

Analyzing Docker Container Forensics

When to Use

  • When investigating a compromised Docker container or container host
  • For analyzing malicious Docker images pulled from registries
  • During incident response involving containerized application breaches
  • When examining container escape attempts or privilege escalation
  • For auditing container configurations and identifying misconfigurations

Prerequisites

  • Docker CLI access on the forensic workstation
  • Access to the Docker host file system (forensic image or live)
  • Understanding of Docker layered file system (overlay2, aufs)
  • dive, docker-explorer, or container-diff for image analysis
  • Knowledge of Docker daemon configuration and socket security
  • Trivy or Grype for vulnerability scanning of container images

Workflow

Step 1: Preserve Container State and Evidence
bash
# List all containers (including stopped)
docker ps -a --no-trunc > /cases/case-2024-001/docker/container_list.txt

# Inspect the compromised container
CONTAINER_ID="abc123def456"
docker inspect $CONTAINER_ID > /cases/case-2024-001/docker/container_inspect.json

# Export container filesystem as tarball (preserves current state)
docker export $CONTAINER_ID > /cases/case-2024-001/docker/container_export.tar

# Create an image from the container's current state
docker commit $CONTAINER_ID forensic-evidence:case-2024-001
docker save forensic-evidence:case-2024-001 > /cases/case-2024-001/docker/container_image.tar

# Capture container logs
docker logs $CONTAINER_ID --timestamps > /cases/case-2024-001/docker/container_logs.txt 2>&1

# Capture running processes (if container is still running)
docker top $CONTAINER_ID > /cases/case-2024-001/docker/container_processes.txt

# Capture network connections
docker exec $CONTAINER_ID netstat -tlnp 2>/dev/null > /cases/case-2024-001/docker/container_network.txt

# Copy specific files from the container
docker cp $CONTAINER_ID:/var/log/ /cases/case-2024-001/docker/container_var_log/
docker cp $CONTAINER_ID:/tmp/ /cases/case-2024-001/docker/container_tmp/
docker cp $CONTAINER_ID:/etc/passwd /cases/case-2024-001/docker/container_passwd

# Hash all exported evidence
sha256sum /cases/case-2024-001/docker/*.tar > /cases/case-2024-001/docker/evidence_hashes.txt
Step 2: Analyze Container Image Layers
bash
# Install dive for image layer analysis
wget https://github.com/wagoodman/dive/releases/latest/download/dive_linux_amd64.deb
sudo dpkg -i dive_linux_amd64.deb

# Analyze image layers interactively
dive forensic-evidence:case-2024-001

# Non-interactive layer analysis
dive forensic-evidence:case-2024-001 --ci --json /cases/case-2024-001/docker/dive_analysis.json

# Extract and examine individual layers
mkdir -p /cases/case-2024-001/docker/layers/
tar -xf /cases/case-2024-001/docker/container_image.tar -C /cases/case-2024-001/docker/layers/

# List the image manifest and layer order
cat /cases/case-2024-001/docker/layers/manifest.json | python3 -m json.tool

# Examine each layer for changes
for layer in /cases/case-2024-001/docker/layers/*/layer.tar; do
    echo "=== Layer: $(dirname $layer | xargs basename) ==="
    tar -tf "$layer" | head -20
    echo "..."
done

# Use container-diff to compare with original base image
# Install container-diff
curl -LO https://storage.googleapis.com/container-diff/latest/container-diff-linux-amd64
chmod +x container-diff-linux-amd64

# Compare committed image with original
./container-diff-linux-amd64 diff daemon://nginx:latest daemon://forensic-evidence:case-2024-001 \
   --type=file --type=apt --type=history --json \
   > /cases/case-2024-001/docker/container_diff.json
Step 3: Examine Docker Host Artifacts
bash
# Docker data directory (default: /var/lib/docker/)
DOCKER_ROOT="/mnt/evidence/var/lib/docker"

# Examine overlay2 filesystem layers
ls -la $DOCKER_ROOT/overlay2/

# Find the container's merged filesystem
CONTAINER_HASH=$(docker inspect $CONTAINER_ID --format '{{.GraphDriver.Data.MergedDir}}' 2>/dev/null)
# Or manually from forensic image:
# Look in /var/lib/docker/containers/<container_id>/config.v2.json

# Analyze container configuration files
cat $DOCKER_ROOT/containers/$CONTAINER_ID/config.v2.json | python3 -m json.tool \
   > /cases/case-2024-001/docker/container_config.json

# Check Docker daemon configuration
cat /mnt/evidence/etc/docker/daemon.json 2>/dev/null > /cases/case-2024-001/docker/daemon_config.json

# Examine Docker events log
cat $DOCKER_ROOT/containers/$CONTAINER_ID/*.log > /cases/case-2024-001/docker/container_json_logs.txt

# Check for volume mounts (potential host filesystem access)
python3 << 'PYEOF'
import json

with open('/cases/case-2024-001/docker/container_inspect.json') as f:
    data = json.load(f)

inspect = data[0] if isinstance(data, list) else data

print("=== CONTAINER SECURITY ANALYSIS ===\n")

# Check mounts
print("Volume Mounts:")
for mount in inspect.get('Mounts', []):
    rw = "READ-WRITE" if mount.get('RW') else "READ-ONLY"
    print(f"  {mount.get('Source', 'N/A')} -> {mount.get('Destination', 'N/A')} ({rw})")
    if mount.get('Source') in ('/', '/etc', '/var', '/root') and mount.get('RW'):
        print(f"    WARNING: Sensitive host path mounted read-write!")

# Check privileged mode
host_config = inspect.get('HostConfig', {})
if host_config.get('Privileged'):
    print("\nWARNING: Container was running in PRIVILEGED mode!")

# Check capabilities
cap_add = host_config.get('CapAdd', [])
if cap_add:
    print(f"\nAdded Capabilities: {cap_add}")
    dangerous_caps = ['SYS_ADMIN', 'SYS_PTRACE', 'NET_ADMIN', 'SYS_MODULE']
    for cap in cap_add:
        if cap in dangerous_caps:
            print(f"  WARNING: Dangerous capability: {cap}")

# Check PID namespace
if host_config.get('PidMode') == 'host':
    print("\nWARNING: Container shares host PID namespace!")

# Check network mode
if host_config.get('NetworkMode') == 'host':
    print("\nWARNING: Container shares host network namespace!")

# Check user
user = inspect.get('Config', {}).get('User', 'root (default)')
print(f"\nRunning as user: {user}")

# Check environment variables for secrets
env_vars = inspect.get('Config', {}).get('Env', [])
print(f"\nEnvironment Variables: {len(env_vars)}")
for env in env_vars:
    key = env.split('=')[0]
    if any(s in key.upper() for s in ['PASSWORD', 'SECRET', 'KEY', 'TOKEN', 'CREDENTIAL']):
        print(f"  SENSITIVE: {key}=***REDACTED***")
PYEOF
Step 4: Analyze Container File System Changes
bash
# Compare container filesystem to original image
docker diff $CONTAINER_ID > /cases/case-2024-001/docker/filesystem_changes.txt

# A = Added, C = Changed, D = Deleted
# Analyze changes
python3 << 'PYEOF'
added = []
changed = []
deleted = []

with open('/cases/case-2024-001/docker/filesystem_changes.txt') as f:
    for line in f:
        line = line.strip()
        if line.startswith('A '):
            added.append(line[2:])
        elif line.startswith('C '):
            changed.append(line[2:])
        elif line.startswith('D '):
            deleted.append(line[2:])

print(f"Files Added: {len(added)}")
print(f"Files Changed: {len(changed)}")
print(f"Files Deleted: {len(deleted)}")

# Flag suspicious additions
suspicious = [f for f in added if any(s in f for s in
    ['/tmp/', '/dev/shm/', '/root/', '.sh', '.py', '.elf', 'reverse', 'shell', 'backdoor'])]
if suspicious:
    print(f"\nSuspicious Added Files:")
    for f in suspicious:
        print(f"  {f}")

# Flag suspicious changes
sus_changed = [f for f in changed if any(s in f for s in
    ['/etc/passwd', '/etc/shadow', '/etc/crontab', '/etc/ssh', '.bashrc'])]
if sus_changed:
    print(f"\nSuspicious Changed Files:")
    for f in sus_changed:
        print(f"  {f}")
PYEOF

# Extract and examine the container export
mkdir -p /cases/case-2024-001/docker/container_fs/
tar -xf /cases/case-2024-001/docker/container_export.tar -C /cases/case-2024-001/docker/container_fs/

# Scan for webshells and malicious files
find /cases/case-2024-001/docker/container_fs/tmp/ -type f -exec file {} \;
find /cases/case-2024-001/docker/container_fs/ -name "*.php" -newer /cases/case-2024-001/docker/container_fs/etc/hostname
Step 5: Scan for Vulnerabilities and Generate Report
bash
# Scan the image for known vulnerabilities
trivy image forensic-evidence:case-2024-001 \
   --format json \
   --output /cases/case-2024-001/docker/vulnerability_scan.json

# Scan the exported filesystem
trivy fs /cases/case-2024-001/docker/container_fs/ \
   --format table \
   --output /cases/case-2024-001/docker/fs_vulnerabilities.txt

# Check for secrets in the image
trivy image forensic-evidence:case-2024-001 \
   --scanners secret \
   --format json \
   --output /cases/case-2024-001/docker/secrets_scan.json

Key Concepts

ConceptDescription
Image layersRead-only filesystem layers stacked to form the container image
overlay2Default Docker storage driver using union filesystem for layers
Container diffComparison of runtime filesystem changes against the original image
Privileged modeContainer with full host capabilities (bypasses most isolation)
Docker socketUnix socket (/var/run/docker.sock) controlling the Docker daemon
Container escapeTechnique for breaking out of container isolation to the host
Volume mountsHost filesystem paths made accessible inside the container
Image historyRecord of Dockerfile instructions used to build each layer

Tools & Systems

ToolPurpose
docker inspectDetailed container configuration and state information
docker diffShow filesystem changes made in a running/stopped container
diveInteractive Docker image layer analysis tool
container-diffGoogle tool for comparing container image contents
TrivyVulnerability scanner for container images and filesystems
docker-explorerForensic tool for offline Docker artifact analysis
SysdigContainer runtime security monitoring and forensics
FalcoRuntime threat detection for containers and Kubernetes
Show full SKILL.md (174 more words)Show less

Common Scenarios

Scenario 1: Web Application Container Compromise Export the container filesystem, identify webshells in web root, analyze access logs for exploitation attempts, check for added files and modified configurations, examine network connections for C2 communication, review container capabilities for escalation paths.

Scenario 2: Supply Chain Attack via Malicious Image Analyze image layers with dive to identify which layer added malicious content, compare with the official base image using container-diff, check image history for suspicious RUN commands, scan for embedded backdoors and cryptocurrency miners, trace the image pull from registry logs.

Scenario 3: Container Escape Investigation Check if container ran privileged or with dangerous capabilities, examine host filesystem mount points for unauthorized access, review Docker socket mount enabling Docker-in-Docker abuse, analyze host system logs for container escape indicators, check for kernel exploit artifacts.

Scenario 4: Cryptojacking in Container Environment Identify high-CPU containers, export and analyze the container image for mining binaries, check for unauthorized images in the registry, review container creation events for rogue deployments, examine network connections for mining pool communications.

Output Format

Docker Container Forensics Summary:
  Container: abc123def456 (nginx-app)
  Image: company/web-app:v2.1
  Status: Running (started 2024-01-10 09:00 UTC)
  Host: docker-host-01.corp.local

  Security Configuration:
    Privileged: No
    Capabilities Added: NET_ADMIN (WARNING)
    Volume Mounts: /var/log -> /host-logs (RW)
    Network Mode: bridge
    User: root (WARNING)

  Filesystem Changes:
    Added: 23 files (5 suspicious)
    Changed: 12 files (2 suspicious)
    Deleted: 0 files

  Suspicious Findings:
    /tmp/reverse.sh - Reverse shell script (Added)
    /var/www/html/.hidden/shell.php - PHP webshell (Added)
    /etc/crontab - Modified (persistence cron entry added)
    /root/.ssh/authorized_keys - Modified (unauthorized key added)

  Vulnerability Scan:
    Critical: 3 (CVE-2024-xxxx in base image)
    High: 12
    Medium: 34

  Evidence: /cases/case-2024-001/docker/

© mukul975, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts, references) in skills/analyzing-docker-container-forensics of mukul975/Anthropic-Cybersecurity-Skills.

  • SKILL.md
  • LICENSE
  • references/api-reference.md
  • scripts/agent.py

Open the folder on GitHubat commit 54a7988

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Works with

Categories

Questions about Analyzing Docker Container Forensics

What does Analyzing Docker Container Forensics do?

Investigate compromised Docker containers by analyzing images, layers, volumes, logs, and runtime artifacts to identify malicious activity and evidence. Analyzing Docker Container Forensics is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Investigate compromised Docker containers by analyzing images, layers, volumes, logs, and runtime artifacts to identify malicious activity and evidence.

When should I use Analyzing Docker Container Forensics?

Analyzing Docker Container Forensics fits situations like: tasks that involve Containers.

How do I install Analyzing Docker Container Forensics in Claude Code?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-docker-container-forensics -a claude-code`. Or copy the skill folder (skills/analyzing-docker-container-forensics in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/analyzing-docker-container-forensics in your project. Claude Code loads it when a task matches its description.

How do I install Analyzing Docker Container Forensics in Codex?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-docker-container-forensics -a codex`. Or copy the skill folder (skills/analyzing-docker-container-forensics in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/analyzing-docker-container-forensics in your project. Codex loads it when a task matches its description.

Can I use Analyzing Docker Container Forensics in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-docker-container-forensics -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/analyzing-docker-container-forensics, .gemini/skills/analyzing-docker-container-forensics, .github/skills/analyzing-docker-container-forensics and .opencode/skills/analyzing-docker-container-forensics in your project.

What does Analyzing Docker Container Forensics need to run?

Going by SKILL.md and its folder, Analyzing Docker Container Forensics needs Python for the scripts in its folder and the command-line tools its instructions call (docker, python3, trivy, wget and curl). Our summary lists: Python 3; Docker.

Does Analyzing Docker Container Forensics access the network?

SKILL.md names 2 domains. In commands or code: github.com and storage.googleapis.com; the agent is likely to contact these when it follows the instructions. This is read from the text; nothing was executed.

Is Analyzing Docker Container Forensics safe to install?

Our automated static check of SKILL.md found notes only (runs commands with sudo), nothing it rates as a warning. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Analyzing Docker Container Forensics use?

Analyzing Docker Container Forensics is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Analyzing Docker Container Forensics use?

About 3.2k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 729 tokens, read only when the agent opens those files.

What are the alternatives to Analyzing Docker Container Forensics?

Skills that share tags, products or a category with Analyzing Docker Container Forensics: Iron Proxy Gateway for NanoClaw (nanocoai/nanoclaw, 31k stars), GreptimeDB Dev Docker Image (GreptimeTeam/greptimedb, 6.7k stars), Senior DevOps Toolkit (maslennikov-ig/claude-code-orchestrator-kit, 260 stars) and LangBot Deployment Guide (langbot-app/LangBot, 18k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Analyzing Docker Container Forensics?

mukul975 (a GitHub user) maintains it in mukul975/Anthropic-Cybersecurity-Skills, which has 33,922 GitHub stars. The repository holds 637 skills in this directory. The repository was last updated on August 31, 2026.

Source: mukul975/Anthropic-Cybersecurity-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.