Large language model agents are vulnerable to memory‑poisoning attacks, and existing defenses rely on external modules that are memory‑inefficient and computationally heavy. The paper introduces DREAM, a training‑free, agent‑intrinsic memory repair framework that uses weak reactivation to destabilize malicious memories, anomaly detection on activation patterns, and selective suppression of harmful memories while reinforcing benign ones. Experiments show DREAM cuts backdoor attack success by over 95 % and improves runtime and token efficiency compared to A‑MemGuard, while maintaining high task success on poisoned multi‑agent systems.
arXiv:2606. 04329v1 Announce Type: cross Abstract: Memory is a core component of AI agents, enabling them to accumulate knowledge across interactions and improve performance.
By Pritam Dash, Tongyu Ge, Aditi Jain, Tanmay Shah, Zhiwei Shang
As LLM agents increasingly rely on persistent memory for long-horizon and personalized behavior, they can retain and reuse information across interactions, but this also creates a lasting channel thro...
arXiv:2607. 27080v1 Announce Type: cross Abstract: Memory systems allow agents to retain and reuse information from past interactions, but they can also let malicious content persist.
By Xuanze Chen, Xukang Xie, Wentao Fu, Jiajun Zhou, Shanqing Yu, Qi Xuan
arXiv:2610.14431v2 Announce Type: replace-cross
Abstract: Long-term memory enables LLM agents to learn from past interactions but exposes memory formation to adversarial influence. Existing studies p...
By Haowen Pan, Pengyang Shao, Yunshan Ma, Yanzheng Jin, Naixin Zhai, Xiang Wang, Xun Yang
arXiv:2607. 06595v1 Announce Type: cross Abstract: Personal AI agents powered by large language models can reason and act using available tools to access emails, manage calendars, and push code to remote repositories, all with minimal oversight.
By George Torres, Sharad Shrestha, Satyajayant Misra