agentic/code/frameworks/security-engineering/skills/supply-chain-trust/SKILL.md
Decision aid for supply-chain trust beyond CVE/SBOM — pinning depth, reproducible builds, snapshot pins, firmware locking, and vendor+hash-lock for critical-path deps.
npx skillsauth add jmagly/aiwg supply-chain-trustInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Decision aid for verifying that the code you ship is the code you think you're shipping. Use when designing or reviewing systems whose threat model includes supply-chain compromise (build host, dependency mirror, package registry, firmware) — which is most production systems in 2026.
The skill is distinct from sdlc-complete's SBOM and CVE-scanning capabilities. SBOM tells you what is there. CVE scanning tells you what is known-broken. This skill answers "can I prove that what I shipped is what I expected, end-to-end" — which neither of the others does.
sdlc-complete/skills/security-audit)Layer 4: Hardware / firmware
↑ (does the chip do what it says?)
Layer 3: Build environment
↑ (was the binary produced from the source we think?)
Layer 2: Source dependencies
↑ (did we get the source we expected?)
Layer 1: Source we wrote
↑ (we can audit this)
Each layer has its own attack surface and its own mitigations:
| Layer | Attack | Mitigation | |---|---|---| | 1 (own source) | malicious commit by insider | code review, signed commits, branch protection | | 2 (deps) | typosquat, compromised maintainer, malicious update | pinning + hash-lock + vendoring; transparency logs | | 3 (build) | compromised CI, build host malware | reproducible builds, signed-and-attested artifacts (in-toto, SLSA) | | 4 (hardware) | malicious firmware, supply-chain implant | measured boot, vendor diversity, audit |
A system is only as trustworthy as the weakest layer in the chain it actually depends on.
requirements.txtFor each language ecosystem:
| Ecosystem | Default tool | What it produces |
|---|---|---|
| Python | pip-tools (pip-compile --generate-hashes) → requirements.txt with hashes | package==1.2.3 --hash=sha256:abc... per dep, including transitive |
| Node | npm ci against package-lock.json (with integrity field), or pnpm | per-package SHA-512 integrity |
| Rust | cargo lock file (Cargo.lock) — versions only; for hashes use cargo vet |
| Go | go.mod + go.sum (per-module SHA-256) |
| Ruby | Bundler lockfile + bundle config set --local frozen true |
| Maven/Gradle | dependency:resolve-plugin with checksum verification |
Required: every dependency, including transitive, must have a hash in your lockfile. If a tool can't produce hashed locks, vendor the dep.
For dependencies in security-sensitive paths (auth, crypto, key handling), go further than hash-locking — vendor them:
your-repo/
vendor/
python-fido2-1.1.3/ # checked-in source of the dep
cryptography-41.0.7/ # ditto
...
Cargo.toml or pyproject.toml # references vendor/ paths, not registry
Why vendoring on top of hash-locking:
vendor/Cost: you own update cadence. Acceptable for security-critical paths; impractical for everything.
.deb whose SHA came from poisoned apt repo (review M10)Original: bootstrap verifies SHA-256 of each downloaded .deb, but the SHA-256 manifest itself comes from apt-cache against the build-time apt repo.
What this skill flags: the verification is circular within the same trust domain. If apt is compromised, the manifest is compromised, the SHAs are compromised, all three layers verify a poisoned package.
Remediation:
snapshot.debian.org URLs with a specific timestamp (http://snapshot.debian.org/archive/debian/20260301T000000Z/) — content-addressed, immutableRelease file with explicit Debian archive signing keys, ground-truth at the timestamp.deb URLs discovered at that timestamp; commit the resulting SHA manifest to your signed git repoThe hash of the package must come from a separately-trusted channel than the package itself.
A reproducible build means: given the same source, the same build environment, and the same build inputs, any builder produces a byte-for-byte identical output. This lets you:
| Use case | Approach |
|---|---|
| Bootstrap installers | NixOS — reproducible by construction; the entire system specification is hash-tracked |
| Container images | Bazel + rules_oci OR Nix-built OCI images; pinned base layer with verified digest |
| Linux distro packaging | Reproducible Builds project tooling (reproducible-builds.org); dpkg --reproducible flag in Debian |
| Cross-platform binaries | Static linking, hermetic build env (Bazel), check diffoscope between rebuilds |
# Local rebuild
bazel build //my:artifact
sha256sum bazel-bin/my/artifact
# Compare to CI-published
sha256sum downloaded/artifact.tar.gz
# If they differ, run diffoscope to see what diverges
diffoscope bazel-bin/my/artifact downloaded/artifact.tar.gz
Common reproducibility breakers: timestamps in archives, build-time UUIDs, parallel-build nondeterminism, locale-dependent string sorting. The reproducible-builds.org site catalogs known issues per ecosystem.
For internal tools with a single source-of-truth build host and signed output, the value is marginal. Reproducibility shines when:
SLSA (Supply-chain Levels for Software Artifacts) defines compliance levels:
| Level | What it requires | What it tells consumers | |---|---|---| | 0 | nothing | "this came from somewhere" | | 1 | build process documented | "the build is repeatable in principle" | | 2 | build runs on hosted CI; provenance generated | "we know which CI built it" | | 3 | build is hermetic, isolated; provenance is signed and tamper-evident | "we can prove the build chain end-to-end" | | 4 | additional constraints — two-person review, hermetic builds | "highest assurance" |
Tooling:
cosign (Sigstore) — signs container images and arbitrary blobs; integrates with Rekor transparency logslsa-github-generator — produces SLSA L3 provenance from GitHub Actionsin-toto (CMU) — generic supply-chain attestation frameworkTekton Chains — Kubernetes-native build attestationcosign verify --certificate-identity-regexp=...@my-org \
--certificate-oidc-issuer=https://token.actions.githubusercontent.com \
ghcr.io/my-org/my-image:1.2.3
Pin specific certificate identities and OIDC issuers. A bare cosign verify can be satisfied by any signature.
For systems with hardware-backed cryptographic operations (HSMs, YubiKeys, TPM-equipped hosts), pin to a known-good firmware range. Different firmware versions can have:
Example: YubiKey 5 firmware 5.4 vs 5.7 have different FIDO2 PRF behavior. Code that worked against 5.4 may fail or behave differently on 5.7.
# .aiwg/security-engineering/supply-chain/hardware-pins.yaml
yubikey-5:
tested-firmware: "5.4.3, 5.7.1"
acceptable-firmware: ">=5.4.0, <6.0"
test-vectors: "tests/yubikey-5-prf.txt"
last-validated: "2026-04-15"
tpm-2.0:
vendor: "Infineon"
acceptable-firmware: ">=7.83"
pcr-baseline: ".aiwg/security-engineering/chain-of-trust/pcr-baseline.yaml"
When a new firmware ships, run your test vectors against the new version before allowing it into production.
Original: install-pkgs.sh verifies SHA-256 of each .deb file against a manifest produced by apt-cache at build time.
What this skill flags:
Remediation:
snapshot.debian.org/archive/debian/<timestamp>/ URLs in install scriptsRelease file with explicit archive signing keys (/usr/share/keyrings/debian-archive-keyring.gpg).deb SHA manifest from the snapshot, NOT from apt-cachepython-fido2 and transitive deps in PRF hot pathOriginal: bootstrap installs python-fido2, cryptography, cffi, pyusb from PyPI at first run.
What this skill flags:
Remediation (see also auth-factor-design):
python-fido2 with libfido2 C tools — drops Python from hot pathpython-fido2 + transitive deps with hash-locked wheels in bootstrap blob; verify per-wheel SHA-256 against manifest committed to signed repo; reproducible-build the bootstrapOriginal: design assumes "YubiKey 5 with FIDO2 PRF" without specifying firmware range.
What this skill flags:
Remediation:
Mandatory exercise — for every external input to your build, document:
| Input | Source | Verification | Trust anchor | |---|---|---|---| | Source code (your repo) | git origin | signed commits + signed tags | dev signing keys | | Source code (deps) | language registry | hash-locked manifest | dev publishes verified | | Base OS packages | snapshot.debian.org | Debian archive key | embedded in keyring | | Container base layer | OCI registry | image digest pinned | (none — ASSUMPTION) | | Build CI | GitHub Actions | OIDC + SLSA L3 attestation | GitHub OIDC issuer | | Hardware | physical procurement | (assumption: vendor not compromised) | (none — ASSUMPTION) | | Firmware | (vendor) | version pinned, test vectors run | self-tested |
Rows with "ASSUMPTION" are explicitly accepted risks. Document why each assumption is acceptable.
When invoked as part of a review, produce findings in standard format. When authoring or updating, produce:
supply-chain-pins.yaml — current pins for all layershardware-pins.yaml — firmware versions testedtrust-boundary.md — inventory and assumption acceptancesdlc-complete/templates/security/sbom-guidance.md (SBOM is upstream of this skill)sdlc-complete/templates/security/dependency-policy-template.md (this skill operationalizes the policy)chain-of-trust-design (Layer 3+4 — boot-time integrity)physical-threat-modeling (Layer 4 — hardware implant scenarios)slsa.dev)reproducible-builds.org)data-ai
Report which research-corpus radar sidecars are overdue for refresh. Computes staleness (days since last refresh vs the cadence window) for every radar, sorted most-overdue-first. Runs via `aiwg corpus radar-status`.
data-ai
Aggregate research-corpus radar sidecars into a corpus or per-cluster freshness report — totals, overdue count, per-cluster / per-GRADE / per-trajectory breakdowns, an overdue table, and per-radar rationale snippets. Runs via `aiwg corpus radar-report`.
testing
Scaffold radar/freshness sidecars for research-corpus REFs. Pulls title/authors from the citation sidecar and GRADE from the analysis doc, defaults the refresh cadence from GRADE and the cluster from a corpus-local map, and stamps documentation/radar/REF-XXX-radar.md. Runs via `aiwg corpus radar-init`.
data-ai
Compute an entity's publication trajectory — per-year paper counts, topic drift, hot-streak detection (≥3 consecutive A-grade years), and career phase. Runs via `aiwg corpus profile-temporal`.