Retrieval-augmented generation
AgentPoison
AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases
Superseded baseline#113 of 1,179 most-superseded · first seen Jul 17, 2024
Superseded — cited as a baseline and beaten by newer methods
1 papers critique it · 1 beat it on benchmarks
What papers say
Verbatim critique sentences, each from a paper that cites AgentPoison as a baseline.
these methods inevitably introduce abnormal inference behaviors and new security risks to the deployed LLMs as these distinctive behaviors are generating incorrect results on particular verification prompts/questions
Beaten on benchmarks
Head-to-head results where a newer method reports beating AgentPoison. Values are copied from the source paper's tables — verify against the cited paper.
[name]-O beats AgentPoison
0.10 vs 0.86
What to use instead
Recent methods in the same sub-problem, not yet superseded in the knowledge base — arXiv benchmark leaders, not vetted production recommendations.
- DiscourseFlipDiscourseFlip: An Oblique Discourse-Level Opinion Manipulation Attack against Black-box Retrieval-Augmented GenerationMay 31, 2026
- SilentRetrievalSilentRetrieval: Hijacking Retrieval-Augmented Generation via Semantically-Preserving Adversarial Data PoisoningMay 27, 2026
- Deceptive Evolutionary Jamming Attack (DEJA)Beyond Explicit Refusals: Soft-Failure Attacks on Retrieval-Augmented GenerationApr 20, 2026
- Apr 3, 2026
- Mar 12, 2026
- Feb 6, 2026
- SD-RAGSD-RAG: A Prompt-Injection-Resilient Framework for Selective Disclosure in Retrieval-Augmented GenerationJan 16, 2026
- RIPRAGRIPRAG: Hack a Black-box Retrieval-Augmented Generation Question-Answering System with Reinforcement LearningOct 11, 2025