analyzing-docker-container-forensics

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

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Install Skill

Run in your terminal

$npx skills install mukul975/Anthropic-Cybersecurity-Skills/analyzing-docker-container-forensics

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Installation Guide

How to use analyzing-docker-container-forensics on Cursor

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1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your machine
  • Node.js 16+ with npm — verify with node --version
  • Active project directory where you want to add analyzing-docker-container-forensics
2

Run the install command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills install mukul975/Anthropic-Cybersecurity-Skills/analyzing-docker-container-forensics

Fetches analyzing-docker-container-forensics from mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI shows a list of agents. Use arrow keys and space to select Cursor:

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4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/analyzing-docker-container-forensics

Restart Cursor to activate analyzing-docker-container-forensics. Access via /analyzing-docker-container-forensics in your agent's command palette.

Security Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.

Documentation

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-01 - RS.AN-03 - DE.AE-02 - RS.MA-01

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

# 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

# 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

# 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

# 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

# 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/

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

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Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Steps

  1. 1Install skill using provided installation command
  2. 2Test with simple use case relevant to your work
  3. 3Evaluate output quality and relevance
  4. 4Iterate on prompts to improve results
  5. 5Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use when

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid when

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Related Skills

Reviews

4.856 reviews
  • C
    Chaitanya PatilDec 28, 2024

    Useful defaults in analyzing-docker-container-forensics — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • M
    Meera TandonDec 24, 2024

    Solid pick for teams standardizing on skills: analyzing-docker-container-forensics is focused, and the summary matches what you get after install.

  • M
    Meera HaddadDec 16, 2024

    Registry listing for analyzing-docker-container-forensics matched our evaluation — installs cleanly and behaves as described in the markdown.

  • M
    Meera FarahDec 12, 2024

    analyzing-docker-container-forensics has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • A
    Ava GonzalezDec 8, 2024

    Useful defaults in analyzing-docker-container-forensics — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • A
    Aanya BansalDec 4, 2024

    I recommend analyzing-docker-container-forensics for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • M
    Meera DialloNov 27, 2024

    analyzing-docker-container-forensics is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • D
    Dev RamirezNov 23, 2024

    Keeps context tight: analyzing-docker-container-forensics is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • P
    Piyush GNov 19, 2024

    analyzing-docker-container-forensics is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • M
    Meera MartinezNov 19, 2024

    Solid pick for teams standardizing on skills: analyzing-docker-container-forensics is focused, and the summary matches what you get after install.

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