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RESEARCH RECORD

AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases

Zhaorun Chen · Zhen Xiang · Chaowei Xiao · Dawn Song · Bo Li · University of Chicago; UIUC; University of Wisconsin-Madison; UC Berkeley

Original publication ↗ · BibTeX · RIS

The reported experiments show that benign-task accuracy can coexist with triggered failures, so average ordinary-task utility does not alone establish retrieval integrity.

Research question

Can an agent remain useful on benign queries while retrieved memory steers it incorrectly?

Design & assumptions

The paper evaluates poisoned memory or knowledge bases across driving, question-answering and health-record agents, separating retrieval, action and end-to-end outcomes.

Findings

The reported experiments show that benign-task accuracy can coexist with triggered failures, so average ordinary-task utility does not alone establish retrieval integrity.

Limits & counterevidence

The threat model requires influence over stored retrieval material and specified triggers. Retrieval success, action selection and end-to-end success are distinct metrics, not interchangeable attack rates.

Source evidence

No structured numerical extraction is available in this reading package.

Reading scope & review

Selected original HTML sections and table context; not a complete paper/appendix audit or independent reproduction.

Model-assisted reading; independent evidence review pending.

Check the original source ↗