performing-container-image-hardening
This skill covers hardening container images by minimizing attack surface, removing unnecessary packages, implementing multi-stage builds, configuring non-root users, and applying CIS Docker Benchmark recommendations to produce secure production-ready images.
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Installation Guide
How to use performing-container-image-hardening on Cursor
AI-first code editor with Composer
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
performing-container-image-hardening
Run the install command
Execute the skills CLI command in your project's root directory to begin installation:
Fetches performing-container-image-hardening from mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate performing-container-image-hardening. Access via /performing-container-image-hardening 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 | performing-container-image-hardening |
| description | 'This skill covers hardening container images by minimizing attack surface, removing unnecessary packages, implementing multi-stage builds, configuring non-root users, and applying CIS Docker Benchmark recommendations to produce secure production-ready images. ' |
| domain | cybersecurity |
| subdomain | devsecops |
| tags | - devsecops - cicd - container-hardening - docker - cis-benchmark - secure-sdlc |
| version | 1.0.0 |
| author | mahipal |
| license | Apache-2.0 |
| nist_csf | - PR.PS-01 - GV.SC-07 - ID.IM-04 - PR.PS-04 |
Performing Container Image Hardening
When to Use
- When building production container images that need minimal attack surface
- When compliance requires CIS Docker Benchmark adherence for container configurations
- When reducing image size to minimize vulnerability exposure from unused packages
- When implementing defense-in-depth for containerized workloads
- When migrating from fat base images to distroless or minimal images
Do not use for runtime container security monitoring (use Falco), for host-level Docker daemon hardening (use CIS Docker Benchmark host checks), or for container orchestration security (use Kubernetes security scanning).
Prerequisites
- Docker or BuildKit for multi-stage builds
- Base image options: distroless, Alpine, slim, or scratch
- Container scanning tool (Trivy) for validation
- CIS Docker Benchmark reference
Workflow
Step 1: Use Multi-Stage Builds to Minimize Image Size
# Build stage with all dependencies
FROM python:3.12-bookworm AS builder
WORKDIR /build
COPY requirements.txt .
RUN pip install --no-cache-dir --prefix=/install -r requirements.txt
COPY src/ ./src/
RUN python -m compileall src/
# Production stage with minimal base
FROM python:3.12-slim-bookworm AS production
RUN apt-get update && \
apt-get install -y --no-install-recommends libpq5 && \
rm -rf /var/lib/apt/lists/* && \
apt-get purge -y --auto-remove -o APT::AutoRemove::RecommendsImportant=false
COPY --from=builder /install /usr/local
COPY --from=builder /build/src /app/src
RUN groupadd -r appuser && useradd -r -g appuser -d /app -s /sbin/nologin appuser
RUN chown -R appuser:appuser /app
USER appuser
WORKDIR /app
HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \
CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:8080/health')" || exit 1
EXPOSE 8080
ENTRYPOINT ["python", "-m", "src.main"]
Step 2: Use Distroless Base Images
# Go application with distroless
FROM golang:1.22 AS builder
WORKDIR /app
COPY go.* ./
RUN go mod download
COPY . .
RUN CGO_ENABLED=0 GOOS=linux go build -ldflags="-w -s" -o /server .
FROM gcr.io/distroless/static-debian12:nonroot
COPY --from=builder /server /server
USER nonroot:nonroot
ENTRYPOINT ["/server"]
Step 3: Remove Unnecessary Components
# Hardened image checklist
FROM ubuntu:24.04 AS base
RUN apt-get update && \
apt-get install -y --no-install-recommends \
ca-certificates \
libssl3 && \
# Remove package manager to prevent runtime package installation
apt-get purge -y --auto-remove apt dpkg && \
rm -rf /var/lib/apt/lists/* \
/var/cache/apt/* \
/tmp/* \
/var/tmp/* \
/usr/share/doc/* \
/usr/share/man/* \
/usr/share/info/* \
/root/.cache
# Remove shells if not needed
RUN rm -f /bin/sh /bin/bash /usr/bin/sh 2>/dev/null || true
# Remove setuid/setgid binaries
RUN find / -perm /6000 -type f -exec chmod a-s {} + 2>/dev/null || true
Step 4: Configure Read-Only Filesystem
# Kubernetes deployment with read-only root filesystem
apiVersion: apps/v1
kind: Deployment
metadata:
name: hardened-app
spec:
template:
spec:
securityContext:
runAsNonRoot: true
runAsUser: 65534
fsGroup: 65534
seccompProfile:
type: RuntimeDefault
containers:
- name: app
image: app:hardened
securityContext:
allowPrivilegeEscalation: false
readOnlyRootFilesystem: true
capabilities:
drop: ["ALL"]
volumeMounts:
- name: tmp
mountPath: /tmp
- name: cache
mountPath: /app/cache
volumes:
- name: tmp
emptyDir:
sizeLimit: 100Mi
- name: cache
emptyDir:
sizeLimit: 50Mi
Step 5: Pin Base Image by Digest
# Pin to exact image digest for reproducibility
FROM python:3.12-slim-bookworm@sha256:abcdef1234567890 AS production
# This ensures the exact same base image is used every time
Step 6: Validate Hardening with Automated Scanning
# Scan hardened image with Trivy
trivy image --severity HIGH,CRITICAL hardened-app:latest
# Check CIS Docker Benchmark compliance
docker run --rm -v /var/run/docker.sock:/var/run/docker.sock \
aquasec/docker-bench-security
# Verify no root processes
docker run --rm hardened-app:latest whoami
# Expected: appuser (NOT root)
# Verify read-only filesystem
docker run --rm hardened-app:latest touch /test 2>&1
# Expected: Read-only file system error
Key Concepts
| Term | Definition |
|---|---|
| Multi-Stage Build | Docker build technique using multiple FROM stages to separate build and runtime, reducing final image size |
| Distroless | Google-maintained minimal container images containing only the application and runtime dependencies |
| Non-Root User | Running container processes as unprivileged user to limit impact of container escape exploits |
| Read-Only Root | Mounting the container root filesystem as read-only to prevent runtime modification |
| Image Digest | SHA256 hash uniquely identifying an exact image version, more precise than mutable tags |
| Scratch Image | Empty Docker base image used for statically compiled binaries requiring no OS |
| Security Context | Kubernetes pod/container-level security settings controlling privileges, filesystem, and capabilities |
Tools & Systems
- Docker BuildKit: Advanced Docker build engine supporting multi-stage builds and build secrets
- Distroless Images: Google's minimal container base images (static, base, java, python, nodejs)
- docker-bench-security: Script checking CIS Docker Benchmark compliance
- Trivy: Container image vulnerability and misconfiguration scanner
- Hadolint: Dockerfile linter enforcing best practices
Common Scenarios
Scenario: Reducing a 1.2GB Python Image to Under 150MB
Context: A data science team uses python:3.12 as base image (1.2GB) with scientific computing packages. The image has 200+ known CVEs from unnecessary system packages.
Approach:
- Switch to
python:3.12-slim-bookwormas base (150MB) and install only required system libraries - Use multi-stage build: compile C extensions in builder stage, copy wheels to production
- Pin numpy, pandas, and scipy to pre-built wheels to avoid build dependencies in production
- Remove pip, setuptools, and wheel from the final image
- Create non-root user and set filesystem permissions
- Validate with Trivy: expect CVE count to drop from 200+ to under 20
Pitfalls: Some Python packages require shared libraries at runtime (libgomp, libstdc++). Test the application thoroughly after removing system packages. Alpine-based images use musl libc which can cause compatibility issues with numpy and pandas.
Output Format
Container Image Hardening Report
==================================
Image: app:hardened
Base: python:3.12-slim-bookworm
Date: 2026-02-23
SIZE COMPARISON:
Before hardening: 1,247 MB (python:3.12)
After hardening: 143 MB (python:3.12-slim + multi-stage)
Reduction: 88.5%
SECURITY CHECKS:
[PASS] Non-root user configured (appuser:1000)
[PASS] HEALTHCHECK instruction present
[PASS] No setuid/setgid binaries found
[PASS] Package manager removed
[PASS] Base image pinned by digest
[PASS] No shell access (/bin/sh removed)
[WARN] /tmp writable (emptyDir mounted)
VULNERABILITY COMPARISON:
Before: 234 CVEs (12 Critical, 45 High)
After: 18 CVEs (0 Critical, 3 High)
Reduction: 92.3%
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Use Cases
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
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
- 1Install skill using provided installation command
- 2Test with simple use case relevant to your work
- 3Evaluate output quality and relevance
- 4Iterate on prompts to improve results
- 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
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
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Reviews
- AAnika Patel★★★★★Dec 24, 2024
performing-container-image-hardening is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- PPratham Ware★★★★★Dec 4, 2024
performing-container-image-hardening fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- KKiara Johnson★★★★★Dec 4, 2024
Solid pick for teams standardizing on skills: performing-container-image-hardening is focused, and the summary matches what you get after install.
- YYash Thakker★★★★★Nov 23, 2024
Registry listing for performing-container-image-hardening matched our evaluation — installs cleanly and behaves as described in the markdown.
- KKofi Tandon★★★★★Nov 15, 2024
Useful defaults in performing-container-image-hardening — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- DDhruvi Jain★★★★★Oct 14, 2024
performing-container-image-hardening reduced setup friction for our internal harness; good balance of opinion and flexibility.
- KKofi Wang★★★★★Oct 6, 2024
I recommend performing-container-image-hardening for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- AAdvait Sethi★★★★★Sep 25, 2024
performing-container-image-hardening reduced setup friction for our internal harness; good balance of opinion and flexibility.
- AAdvait Shah★★★★★Sep 9, 2024
performing-container-image-hardening fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- OOshnikdeep★★★★★Sep 5, 2024
I recommend performing-container-image-hardening for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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