Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add UnboundCompute/security-agent-skills --skill auditing-ml-model-supply-chaingit clone --depth 1 https://github.com/UnboundCompute/security-agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/unboundcompute/security-agent-skills/auditing-ml-model-supply-chain)<a href="https://agentmods.dev/skills/unboundcompute/security-agent-skills/auditing-ml-model-supply-chain"><img src="https://agentmods.dev/badge/skills/unboundcompute/security-agent-skills/auditing-ml-model-supply-chain/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/unboundcompute/security-agent-skills/auditing-ml-model-supply-chain"><img src="https://agentmods.dev/badge/skills/unboundcompute/security-agent-skills/auditing-ml-model-supply-chain.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00117 | $0.01505 |
| Opus 5 | $0.00059 | $0.00753 |
| Sonnet 5 | $0.00023 | $0.00301 |
| Haiku 4.5 | $0.00012 | $0.00151 |
Grade A, and why
auditing-ml-model-supply-chain scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Unrestricted tool accesslowExcessive agency
A wildcard tool grant or "run any command" leaves no least-privilege boundary at all.
not: loading one can execute arbitrary code embedded in the file, and even a Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
How it starts
The opening of the file, as written. The whole thing — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Auditing the ML model supply chain: a model file is code you run
A model file is usually treated as inert data, a bag of weights. Many formats are not: loading one can execute arbitrary code embedded in the file, and even a pure-weights model can carry a backdoor that changes behavior on a trigger. The moment your pipeline loads a model someone else produced, that model is untrusted code and untrusted logic entering your system, on the training host, the inference server, or a developer's laptop.
When to use
- You are adding a model, checkpoint, or weights file to a training or inference pipeline.
- You are reviewing where and how models are loaded, and from where.
- You are vetting a third-party or publicly-hosted model before you trust it.
Scope check
Audit models and pipelines you own or are authorized to test. Do not load or execute untrusted model files outside a contained environment. If you can't name the authorization, stop.
The loop
-
Inventory every model load path and its format. List where the system loads a model, checkpoint, or weights file, who produced each one, and the serialization format. Formats that can reconstruct arbitrary objects execute code on load; formats that carry only tensors are safer. Mark each load site by format risk.
-
Check for code execution on load (the RCE leg). For any load path using a format that can rebuild arbitrary objects, a malicious file runs code the instant it is loaded, before any inference. Confirm whether untrusted files reach that path, and whether a tensor-only loader or a restricted deserializer is enforced. An unrestricted load of an externally-sourced file is remote code execution.
-
Trace provenance and integrity. Where did each model come from, and is it verified? Check for a pinned hash or signature, a known publisher, and a fixed version. A model pulled by mutable name from a public hub without hash pinning is a rug-pull and name-confusion channel: the artifact can be swapped, or a lookalike name served.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 126 lines · 117 tokens per session scan A bf6398d682d9
auditing-ml-model-supply-chain is a skill published in the GitHub repository UnboundCompute/security-agent-skills (5 stars, last pushed 2d ago), licensed MIT. It adds 117 tokens to every session and 1,505 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 1 finding (unrestricted tool access). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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