Skill audit report
Attack the ML/LLM supply chain — poisoned models, datasets, plugins, and unsafe model deserialization. Load when an app loads third-party models/weights (HuggingFace, .pt/.pkl/.h5), installs ML deps, uses plugins/extensions, or fine-tunes on external data. Signals: torch.load, pickle model files, model hub downloads, plugin marketplace, RAG over external corpora.
OpenAgentSkill Trust Score
The Trust Score helps an agent decide whether a skill is safe enough to shortlist before installation.
GitHub adoption
FAIL30
20 GitHub stars
Stars/forks activity
FAIL32
20 stars, 7 forks; issue activity unavailable in current metadata
Recent maintenance
PASS100
1d since push
License clarity
PASS86
MIT
README/SKILL.md completeness
PASS86
Metadata includes enough usage and workflow context
Dependency/runtime risk
PASS90
no major dependency risk hints in public metadata
Install availability
PASS92
npx skills add NoorQureshi/SploitAgent --skill ai-supply-chain
Install command safety
PASS92
standard package or runtime install path
Permission surface
PASS86
filesystem or document access
Repository evidence
PASS86
https://github.com/NoorQureshi/SploitAgent/tree/main/skills/ai-ml/ai-supply-chain
Review status
WARN46
AI review approval is missing
Agent Proven outcomes
INFO54
No agent outcome data yet
Checks
Install path
92
npx skills add NoorQureshi/SploitAgent --skill ai-supply-chain
Repository
88
https://github.com/NoorQureshi/SploitAgent/tree/main/skills/ai-ml/ai-supply-chain
License
86
MIT
Maintenance
100
1d since push
AI review
55
Review approval is missing
README/SKILL.md completeness
86
Usable description available
Dependency risk
90
no major dependency risk hints in public metadata
Install command safety
92
standard package or runtime install path
Permission surface
86
filesystem or document access
Stars/forks activity
32
20 stars, 7 forks; issue activity unavailable in current metadata
Adoption
42
20 GitHub stars
Warnings
Method
This report combines public metadata, AI review output, repository freshness, install readiness, OpenAgentSkill events, quality scoring, trust checks, and the agent safety gate. It is not a full source-code security review.
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Abuse an LLM agent's tools/functions — coerce it to call tools with attacker-chosen args for SSRF, RCE, data exfil, or privilege abuse. Load when the target is an agent with tools/ function-calling/plugins, MCP servers, code interpreters, or "the assistant can do X". Signals: function-calling, tool schemas, browse/email/query/exec tools, autonomous agents.
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