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. While this has improved the agent's functionality and continuity across tasks, it has also introduced a new attack surface: the agent's own reasoning history. In this paper, we introduce the Forged Amplifying Rationale Memory Attack (FARMA), which poisons an agent's remembered reasoning rather than its factual knowledge. It inserts forged reasoning traces using evasive language th
The increasing sophistication and widespread adoption of LLM agents with persistent memory makes their vulnerabilities to novel attack vectors a pressing concern.
This attack reveals a fundamental security weakness in how LLM agents store and utilize their reasoning, potentially undermining their reliability and trustworthiness in critical applications.
The understanding of LLM agent security expands beyond knowledge poisoning to include the manipulation of their internal reasoning processes, necessitating new defensive strategies.
- · Cybersecurity researchers
- · LLM security solution providers
- · Organizations developing robust AI defense mechanisms
- · LLM agent developers ignoring security
- · Users relying on undefended LLM agents for critical tasks
- · Companies whose LLM agents are compromised
Increased focus on robust memory and reasoning integrity for autonomous AI agents.
Development of new security protocols and architectural changes for LLM agent frameworks.
Potential slowdown in enterprise adoption of general-purpose LLM agents until these vulnerabilities are adequately addressed.
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Read at arXiv cs.AI