by facebookresearch · Agent Tool · ★ 1.3k
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docker run --rm -p 7860:7860 llm_transparency_toolTaken from the project's README on GitHub. Check the security audit before running it.
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 1,254 |
| Forks | 104 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 69/100 |
| Open Issues | 9 |
| Last Updated | 2024-12-03 |
| Created | 2023-12-21 |
| Platforms | python |
| Est. Tokens | ~2k |
Key functionality Choose your model, choose or add your prompt, run the inference. Browse contribution graph. Select the token to build the graph from. Tune the contribution threshold. Select representation of any token after any block. For the representation, see its projection to the output vocabulary, see which tokens were promoted/suppressed but the previous block.
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Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: