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New Attack Exploits LLM Agent Memory for Persistent Malicious Instructions

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a Persistent Memory Poisoning Attack (PMPA) targeting LLM-based agents that utilize memory, tool use, and runtime control. This attack embeds malicious instructions into benign external sources, tricking agents into storing them in persistent memory. Once stored, these poisoned instructions can be retrieved in subsequent sessions, leading to unintended malicious actions and privacy breaches. Evaluations on OpenClaw and Claude Code demonstrated significant success rates for injecting and executing these malicious instructions across various configurations, with defenses showing limited effectiveness against already poisoned memory. AI

IMPACT Highlights critical security vulnerabilities in LLM agent architectures, necessitating robust defense mechanisms.

RANK_REASON Academic paper detailing a new attack vector on LLM agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Attack Exploits LLM Agent Memory for Persistent Malicious Instructions

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Academic paper detailing a new attack vector on LLM agents. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Shuhuai Huang, Jingfeng Zhang, Hong Jia ·

    When Malicious Instructions Persist: Persistent Memory Poisoning Attack on Harness-Based Agents

    arXiv:2609.13889v1 Announce Type: cross Abstract: Harness design has transformed the development of LLM-based agents by integrating memory, tool use, and runtime control. However, this design also introduces security and privacy risks because malicious instructions from external …

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