MemAudit: Post-hoc Auditing of Poisoned Agent Memory via Causal Attribution and Structural Anomaly Detection
arXiv:2605.23723v1 Announce Type: new Abstract: Large language model agents increasingly rely on persistent memory to store past interactions, retrieve relevant demonstrations, and improve long-horizon task execution. However, this memory mechanism also creates a practical security vulnerability: an adversarial user may inject malicious records into the agent's memory through ordinary interaction, and these records can later be retrieved to steer the agent's reasoning and actions. Existing defenses primarily focus on online intervention, such as prompt filtering or output blocking, but they do
The increasing reliance of large language model agents on persistent memory makes them vulnerable to malicious data injection, prompting current research into post-hoc auditing methods.
This research addresses a critical security vulnerability in AI agents, which if unmitigated, could lead to widespread manipulation and distrust in autonomous systems.
The development of effective auditing tools will enable more secure and trustworthy deployment of AI agents by allowing for the detection and mitigation of memory poisoning attacks.
- · AI security researchers
- · Enterprises deploying AI agents
- · Cybersecurity firms
- · Malicious actors
- · Unsecured AI agent developers
Increased trust and adoption of AI agents in critical applications.
New regulatory requirements for memory auditing in AI systems.
The emergence of a specialized market for AI memory forensic tools.
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Read at arXiv cs.AI