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AI/LLM supply chain security refers to safeguarding the entire lifecycle of LLMs (Large Language Models) and AI components, addressing risks found in dependencies, containers, drivers, frameworks, models, and datasets. Attackers may target any point in this chain, so organizations must ensure every artifact, whether a model file, runner, or driver, can be verified, tracked, and defended like traditional high-risk IT systems.
Provenance provides verifiable evidence of where a model, dataset, or container originated, detailing who built it, when, and with what methods or materials. This record-keeping is essential to ensure models are auditable, help with compliance, and defend against supply chain attacks that can occur when arbitrary or untrusted components are introduced.
Organizations should mandate that every critical artifact—such as model weights and containers—are both signed (using cryptographic signature tools like Cosign) and pinned to explicit digests before deployment. Verification must happen at deploy/admission time so only trusted, unaltered assets enter production.
An SBOM (Software Bill of Materials) catalogs all software components and dependencies included in a build or deployment. For AI, classic SBOMs are needed for the runtime (runners, frameworks), while extended versions like MLBOMs or AI-BOMs track model-specific items—weights, datasets, tokenizer, transforms, and more—so teams know exactly what is running and can trace changes or respond to incidents quickly.
Model weights are executable data. Unsigned or unpinned weight files can be swapped for tampered ones that might leak data, introduce backdoors, or malfunction. Always require cryptographic checksums and signatures; never load arbitrary safetensors or model files from unknown sources.
An MLBOM (Machine Learning Bill of Materials) or AI-BOM records every component—base model, weights, dataset versions, training parameters, transforms, and critical dependencies—for a model or pipeline. This granular metadata enables traceability, compliance, and rapid incident response in the event a model’s integrity or performance is questioned.
AI infrastructure depends on consistent versions across GPUs, CUDA, drivers, and runners. Drift between these can cause failures or expose vulnerabilities. Always lock compatible versions and control upgrades, ensuring critical updates are rapidly patched but changes are validated before deployment.
LLM inference should be isolated to contain security and data risks. Multi-tenant runners can leak data or experience noisy neighbor issues. Deploy models in isolated environments or VPCs, enforce authentication, and use robust network controls to avoid accidental cross-tenant exposure.
Emit runtime attestations for every request—such as model hash and runner version—allowing operational teams to link user actions to specific model states. Security metrics (like failed signature verification) should trigger alerts to catch compromise attempts as they happen.
Uncontrolled telemetry, logs, or integrations can accidentally leak sensitive prompt or output data outside regulated regions, risking compliance violations. Explicitly diagram and restrict data flows for each model, disable verbose logs, and audit third-party connectors to prevent unintentional egress.

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Koushik M.
"Exceptional Hands-On Security Learning Platform"

Varunsainadh K.
"Practical Security Training with Real-World Labs"

Gaël Z.
"A new generation platform showing both attacks and remediations"

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.png)



Koushik M.
"Exceptional Hands-On Security Learning Platform"

Varunsainadh K.
"Practical Security Training with Real-World Labs"

Gaël Z.
"A new generation platform showing both attacks and remediations"

Nanak S.
"Best resource to learn for appsec and product security"




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