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Analyzing Docker Container Forensics

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mukul975
analyzing-docker-container-forensics

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

Overview

Publishermukul975
RepositoryAnthropic-Cybersecurity-Skills
Skill nameanalyzing-docker-container-forensics
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32.9K
Forks
4K
Bundled files
2
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by mukul975 on GitHub. Read the source before you install it.

Installation

Install the Analyzing Docker Container Forensics AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git /tmp/Anthropic-Cybersecurity-Skills
mkdir -p .claude/skills
cp -r /tmp/Anthropic-Cybersecurity-Skills/skills/analyzing-docker-container-forensics .claude/skills/analyzing-docker-container-forensics
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Analyzing Docker Container Forensics in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Analyzing Docker Container Forensics on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Analyzing Docker Container Forensics is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

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/

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Analyzing Docker Container Forensics AI skill do?

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

Why use Analyzing Docker Container Forensics on TypingMind?

Because you install it once and use it with any model. Analyzing Docker Container Forensics is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Analyzing Docker Container Forensics in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-docker-container-forensics. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Analyzing Docker Container Forensics?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Analyzing Docker Container Forensics?

As many as you like. As long as a model supports skills, you can use Analyzing Docker Container Forensics with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Analyzing Docker Container Forensics AI skill free?

Yes. It is published on GitHub by mukul975 under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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