Bad Memory: Evaluating Prompt Injection Risks from Memory in Agentic Systems
Study maps how persistent memory in LLM agents turns a single prompt injection into a durable, cross-session compromise — a threat model enterprises deploying autonomous agents must urgently address.
Summary written by editorial AI · Source link below
arXiv:2607.14611v1 Announce Type: new Abstract: A growing class of agentic systems maintain persistent state across sessions through memory files, behavioral preferences, and knowledge bases. While this makes agents more useful and self-improving, it also creates a new attack surface for prompt injections in which malicious instructions can be embedded within persistent files and influence future behavior. In this work, we study prompt injection attacks in memory-based agentic systems using a s
Editorial Analysis
Enterprises adopting agentic AI need to recognise that persistent memory transforms ephemeral prompt-injection risks into lasting compromises of agent behaviour.
Review all agentic AI deployments for persistent memory stores and implement integrity checks and expiry policies.
Agentic AI systems that remember across sessions can be permanently subverted by a single attack — a new risk requiring governance attention.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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