Asi performing-container-image-hardening
install
source · Clone the upstream repo
git clone https://github.com/plurigrid/asi
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/plurigrid/asi "$T" && mkdir -p ~/.claude/skills && cp -r "$T/plugins/asi/skills/performing-container-image-hardening" ~/.claude/skills/plurigrid-asi-performing-container-image-hardening && rm -rf "$T"
manifest:
plugins/asi/skills/performing-container-image-hardening/SKILL.mdsource content
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
as base (150MB) and install only required system librariespython:3.12-slim-bookworm - 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%