Software, Data & Model Supply Chain
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Code Signing & AI Supply Chain Security Interview Questions
Code and model provenance, dependency analysis (SCA), secret scanning and CI/CD security. Review build authority, artifact promotion, infrastructure evidence and verified repairs across the software, data and model supply chain.
Worked answers that start from a threat model, explain each control by mechanism, and include the follow-ups and scoring an interviewer uses.
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01–12Foundationsthe mechanisms and vocabulary an interviewer expects you to have0/12 done
13–23Core questionsthe questions this round asks most, answered in full0/11 done
24–29Field scenariosdesign rounds, incidents and judgment calls with the inputs left incomplete0/6 done
The concepts behind Software, Data & Model Supply Chain
The lessons these answers build on. Introductory ones are free; advanced ones need Premium.
Foundational
How Code Signing WorksCode signing attaches a digital signature to software so that anyone installing it can verify two things: that it has not been modified since it was signed, and which key signed it, usually tied to a publisher by a certificate. Operating systems, app stores and package managers check these signatures before running code. Code signing does not prove the code is safe: a compromised build signs malware perfectly, and a stolen key lets attackers sign as the publisher. Key protection, timestamping and build integrity carry the real weight.Foundational
Sigstore and Keyless SigningSigstore is an open-source signing system that replaces long-lived signing keys with short-lived certificates bound to an identity, such as a developer's email or a CI workflow, proven through OpenID Connect. Every signing event is recorded in a public, append-only transparency log. Verifiers check not just a signature but who signed and that the event was logged. It is used for container images, npm and PyPI packages, and model signing.Foundational
Unsafe Model Files: Pickle versus SafetensorsMany machine-learning checkpoints have been saved with Python's pickle, a format whose loader can execute code chosen by the file's author, so loading an untrusted model can compromise the machine. Safetensors stores only tensors and a small JSON header, so loading cannot run code. It fixes that one problem: it does not detect backdoored weights, malicious custom code shipped alongside a model, or poisoned training. A safe model intake process combines safe formats, restricted loaders, scanning, provenance and isolation.Core
Package Ecosystem Attacks: npm, PyPI and BeyondInstalling a package means trusting its maintainers, their accounts, their build pipeline and the registry. Attackers abuse each link: look-alike names, private names shadowed by public ones, phished maintainers, poisoned CI that publishes with valid provenance, and worms that steal tokens and republish. Most modern payloads target CI secrets. Effective controls are layered: one index or scoped registries, lockfiles with hashes, blocked install scripts, release cooldowns, behavioural malware detection and CI jobs that never hold secrets while installing.Sign in
Core
SLSA and Build ProvenanceBuild provenance is a signed, machine-readable statement of how an artifact was produced: which source revision, which build platform and process, and with which inputs. SLSA (Supply-chain Levels for Software Artifacts) defines levels of assurance for that provenance, from simply existing to being generated by a hardened, isolated build platform that the build itself cannot forge. Verifying provenance closes the gap code signing leaves: a validly signed artifact built from the wrong source.Sign in
Core
SBOMs and AI Bills of MaterialsA software bill of materials (SBOM) is a machine-readable inventory of the components in a piece of software, with versions and identifiers, so you can answer 'are we affected?' when a vulnerability is announced. An AI bill of materials extends that to models, datasets, adapters, prompts and tools, and to the lineage between them: which base model, which training data, which fine-tuning run. Both are inventories, not protections; their value comes from being accurate, current and queried.Sign in
Advanced
Model Registries and Promotion PipelinesA model registry is the supply-chain choke point for AI systems: whatever is promoted there reaches production, and models are executable artifacts that have carried code. Securing it means separating the roles that upload, approve, promote and deploy; ingesting external models by pinned commit into quarantine; scanning with tools that fail closed, since scanners have been bypassed; converting to safe formats in isolation; evaluating and signing; and deploying only by digest through an admission policy that checks signatures and a revocation list. Lineage then lets a later finding be traced to every deployment.🔒 Premium
Foundational
CI/CD Pipeline Security and Build AuthorityPipeline scanning checks the system that builds and releases code. Map untrusted triggers, runner isolation, token scopes, third-party actions and artifact promotion separately from scanning application source.Unlock all 29 answers · $25
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