Hugging Face Trending Papers

Forgetting is Not Always Bad: A Neuro-Inspired Memory Repair Mechanism for Poisoned LLM Agents

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 AI

Oct 12

Forgetting is Not Always Bad: A Neuro-Inspired Memory Repair Mechanism for Poisoned LLM Agents

The paper introduces DREAM, a training‑free, agent‑intrinsic memory repair framework that selectively forgets poisoned memories in large language model agents. DREAM uses perturbation‑induced reactivation, anomaly detection on activation patterns, and dynamic 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 existing defenses.

By Lei Liu, Yunji Liang, Xiaowen Zhang, Jingqi Liu, Qi Li, Bin Guo, Zhiwen Yu

llms agents

arXiv AI

Jun 4

From Untrusted Input to Trusted Memory: A Systematic Study of Memory Poisoning Attacks in LLM Agents

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

llms agents benchmarks safety

Hugging Face Trending Papers

Sep 28

From Attack Success to Attack Severity: Counterfactual Memory Attacks on LLM Agents

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...

llms agents benchmarks

arXiv Computation and Language

21h ago

MemForm: Benchmarking Memory Formation Poisoning in LLM Agents

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

llms agents robotics benchmarks safety

arXiv AI

Jul 31

MemSecBench: Tracking Agent Memory Poisoning from Persistence to Consequence and Repair

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

llms agents benchmarks

arXiv AI

Jul 9

When Agents Remember Too Much: Memory Poisoning Attacks on Large Language Model Agents

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

llms agents benchmarks

arXiv AI

Sep 30

Trojan Hippo Bench: A Dynamic Benchmark for Persistent Memory Attacks and Defenses in LLM Agents

arXiv:2605.01970v4 Announce Type: replace-cross Abstract: Memory systems enable otherwise stateless LLM agents to persist user information across sessions, but also introduce a new attack surface. Th...

By Debeshee Das, Julien Piet, Darya Kaviani, Luca Beurer-Kellner, Florian Tram\`er, David Wagner

llms rag agents multimodal benchmarks safety

arXiv Machine Learning

Jun 10

MemVenom: Triggered Poisoning of Multimodal Memories in Web Agents

arXiv:2606. 10742v1 Announce Type: cross Abstract: External memory has become a core component of modern web agents, enabling long-horizon reasoning through the retrieval of past experiences.

By Yv Zhang, Hao Sun, Hao Fang, Kuofeng Gao, Fan Mo, Bin Chen, Shu-Tao Xia, Yaowei Wang

llms agents computer-vision multimodal safety

arXiv AI

Jul 7

When Claws Remember but Do Not Tell: Stealthy Memory Injection in Persistent Personal Agents

arXiv:2607. 05189v1 Announce Type: cross Abstract: Persistent personal agents combine long-term memory with access to users' external environments, enabling personalized foreground assistance and proactive background execution.

By Yechao Zhang, Shiqian Zhao, Jiawen Zhang, Jie Zhang, Gelei Deng, Xiaogeng Liu, Chaowei Xiao, Tianwei Zhang

llms agents reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jun 25

Defense effectiveness across architectural layers: a mechanistic evaluation of persistent memory attacks on stateful LLM agents

arXiv:2605. 08442v3 Announce Type: replace-cross Abstract: Persistent memory attacks against LLM agents achieve high attack success rates against open-source models.

By Jun Wen Leong

llms rag agents multimodal

arXiv Machine Learning

Jun 25

Forget to Improve: On-Device LLM-Agent Continual Learning via Budget-Curated Memory

arXiv:2606. 25115v1 Announce Type: new Abstract: On-device language-model agents improve by accumulating experience in retrieved memory rather than by updating weights.

By Beining Wu, Zihao Ding, Jun Huang, Yanxiao Zhao

llms agents robotics benchmarks

arXiv AI

Jul 7

Your Agent's Memories Are Not Its Own: Forged Reasoning Attacks on LLM Agent Memory and Defenses

arXiv:2607. 05029v1 Announce Type: cross Abstract: Persistent memory has enabled large language model (LLM) agents to store factual knowledge, prior decisions, reasoning histories, tool usage information, and context.

By Neeraj Karamchandani, Piyush Nagasubramaniam, Sencun Zhu, Dinghao Wu

llms agents

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