Individual skill
supply-chain
Hunt LLM supply-chain compromise (OWASP LLM03:2025) — malicious or backdoored models, datasets, adapters, plugins, MCP servers, and tokenizer / framework dependencies that ship inside an AI-integrated product.
packages/decepticon/decepticon/skills/standard/analyst/supply-chain/SKILL.md
Source commit df879550544d36b6b1e69062f05adae28537adf2
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- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- Yes
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Network or broad shell
- Last checked
- Nov 9, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
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Repository context
- Repository stars (not skill ratings)
- 6,576
- Repository last updated
- Nov 9, 2026
- License
- Apache-2.0
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- agent Repository topic
- ai Repository topic
- cybersecurity Repository topic
- hacking Repository topic
- langchain Repository topic
- langgraph Repository topic
- llm Repository topic
- generative-ai Repository topic
- pentesting Repository topic
- pentest Repository topic
- engineering Derived category
- security Derived category
- security-application Derived category
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# LLM Supply-Chain Compromise (LLM03:2025)
An LLM product's runtime trust boundary spans far more than the
application code: pre-trained weights, fine-tune adapters, embedding
models, tokenizers, vector databases, framework packages, plugin /
MCP servers, and dataset URLs are all attacker-influenced if any of
them is sourced from a public registry. A malicious LoRA adapter or a
typo-squatted ``langchain-foo`` package is indistinguishable from a
legitimate dependency until it fires.
## 1. Recognition signals
- Model name is something like ``-Q4_K_M.gguf`` pulled from HuggingFace.
- Fine-tune adapter or LoRA layered on top of an open-weight base.