arXiv:2605.26154v1 Announce Type: cross Abstract: LLM-driven agents are capable of selecting external tools to complete users' tasks. However, attackers could compromise such process, steering agents toward inappropriate/wrong tools and enabling malicious actions. Most existing attacks primarily manipulate the tool metadata, which is easily detectable by auditing and may lose effectiveness as modern agents increasingly adopt memory modules to refine tool selection policies through accumulated experience. This paper proposes MemMorph, the first attack that bias tool selection by poisoning the a
The proliferation of LLM agents in critical applications is making their vulnerabilities a pressing concern, driving researchers to develop more sophisticated attack vectors.
This development highlights a significant security vulnerability in autonomous AI systems, which could be exploited to manipulate their behavior and compromise tasks they are designed to perform.
The focus of AI agent security shifts from primarily defending against metadata manipulation to addressing memory poisoning, requiring new defenses and auditing methods.
- · AI security researchers
- · Cybersecurity firms specializing in AI
- · Developers of robust AI agent architectures
- · Organizations deploying vulnerable LLM agents
- · Users relying on compromised AI agents
- · Enterprises with inadequate AI security protocols
Attackers can manipulate the tool selection of LLM agents by poisoning their memory, leading to incorrect or malicious actions.
Increased investment in developing secure and auditable memory modules for AI agents will become a priority, shaping future AI development frameworks.
The weaponization of such techniques could lead to sophisticated, hard-to-detect cyberattacks that leverage autonomous AI systems for industrial espionage or infrastructure disruption.
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Read at arXiv cs.AI