Individual skill
data-and-model-poisoning
Hunt LLM training-data and model poisoning (OWASP LLM04:2025) — adversarial inputs that bias future model behaviour through fine-tuning, RLHF, or continuous-learning loops.
packages/decepticon/decepticon/skills/standard/analyst/data-and-model-poisoning/SKILL.md
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- Observed behavior
- Network or broad shell
- Last checked
- Nov 9, 2026
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- 6,582
- Repository last updated
- Nov 10, 2026
- License
- Apache-2.0
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- 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
- data Derived category
- education Derived category
- security Derived category
- security-application Derived category
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# LLM Data and Model Poisoning (LLM04:2025)
Whenever a product writes user-influenced data back into a training,
fine-tuning, or feedback pipeline, the attacker becomes a co-author of
the next model version. Poisoning is distinct from supply-chain
compromise: the malicious weights are produced **by the victim's own
training infrastructure** using attacker-supplied data the application
collected normally.
## 1. Recognition signals
- Public-facing "thumbs up / thumbs down" + free-text feedback that
feeds an RLHF or DPO pipeline.
- "Help us improve" data collection on free-tier accounts.