Agent skill

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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SKILL.md

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

dockerfile
# 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

dockerfile
# 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

dockerfile
# 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

yaml
# 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

dockerfile
# 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

bash
# 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:

  1. Switch to python:3.12-slim-bookworm as base (150MB) and install only required system libraries
  2. Use multi-stage build: compile C extensions in builder stage, copy wheels to production
  3. Pin numpy, pandas, and scipy to pre-built wheels to avoid build dependencies in production
  4. Remove pip, setuptools, and wheel from the final image
  5. Create non-root user and set filesystem permissions
  6. 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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