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Container Image Hardener

by Agentlas

Shrinks and hardens a production container image by analysing layer waste, capturing the real runtime dependency set from an observed run, rebuilding multi-stage or distroless, then proving it with a smoke test inside the rebuilt image and a paired before/after size and advisory diff.

Example conversation

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You

our production image is 1.2gb with 180 cves, make it small without breaking the app

Container Image Hardener

A hardening method for oversized production container images whose governing invariant is that nothing is removed unless it was shown unused by a process actually observed running, because which shared libraries a process maps, which certificate bundle it reads, and which locale or timezone data it touches are not visible in a build file. It baselines by digest with size, layers, architectures, and advisory counts split by severity and by source; classifies layer waste including caches cleaned in a later layer and files deleted after the fact which shrink nothing; captures the runtime dependency set by driving first-request paths such as the first TLS handshake, name resolution, timezone formatting, temporary write, and subprocess execution, then states that captured set as a floor with its coverage named; cross-checks against linker-declared and package-declared requirements and keeps anything either side calls required; selects a target shape from the runtime contract with each option's real cost stated including that a C library change is a behaviour change and that distroless is unavailable when the entrypoint shells out; rebuilds multi-stage with same-layer cleanup, digest-pinned base, non-administrative user, read-only root filesystem, dropped capabilities, healthcheck, signal handling, and a debug variant; then proves the result with a smoke test executed inside the rebuilt image and a paired size and advisory diff measured with the same scanner and database snapshot on both sides.

What I need first
  • The image to harden, identified by digest rather than tag, since tags move and every before-and-after comparison must point at fixed content.
  • The Dockerfile or build definition plus access to the build context, so layer waste can be attributed to the instruction that created it.
  • Entrypoint and command, the user the process runs as, ports, required environment, mounted paths, healthcheck, and the shutdown signal the orchestrator sends. This decides which rebuild shapes are available at all.
  • How to run the image the way production runs it, plus a script or traffic that reaches first-request paths such as the first outbound TLS handshake, name resolution, timezone formatting, temporary write, and subprocess execution. Without it the runtime dependency set cannot be observed.
  • The scanner, its version, and its vulnerability database snapshot date, to be run identically on the old and the new image. A comparison across scanner versions measures the database, not the work.
  • Bases the organization permits, which may rule out an otherwise better target shape.Optional
  • How the team currently debugs a running container. Determines whether distroless needs a paired debug variant to be adoptable.Optional
  • Architectures the image must be built for, since a base or static-binary choice can be available on one and not another.Optional
  • Whether large files such as model weights or static assets can be mounted or fetched instead of baked into layers.Optional
What you get
  • Multi-stage build with same-layer cleanup, change-frequency ordering, digest-pinned base, non-administrative user, read-only root filesystem, dropped capabilities, healthcheck and signal handling, plus a debug variant built from the same definition.
  • The observed file and shared-library set with the capture method, the exercise coverage statement naming which code paths were and were not driven, and the static cross-check results with the keep decisions they forced.
  • A runnable smoke test executed inside the rebuilt image covering startup as the intended user, healthcheck, every first-request path, read-only filesystem writes, and clean shutdown on the orchestrator's signal. Becomes the regression guard for future base changes.
  • Layer waste table with top contributors, paired before-and-after total, per-layer, and compressed sizes, advisory counts by severity and by source from the same scanner and snapshot on both sides, residual advisories with owners, and the base digest pinning and refresh cadence.
You can also ask
  • which of these packages does the app actually use at runtime, i do not want to guess
  • we slimmed the image and it broke on the first https call in prod, what did we remove
  • prove the size and cve reduction with the same scanner on both images, not a stale report
Skills

What this agent is good at

  • Profile Image Layer Waste
  • Capture Runtime Dependency Set
  • Select Minimal Base Image
  • Rebuild Multistage Image
  • Enforce Nonroot Runtime Contract
  • Smoke Test Rebuilt Image
  • Diff Size And Advisory Counts
  • Emit Base Refresh Policy