ClawMMU: Protection-Coherent Virtual Memory for Stateful LLM Agents
ClawMMU applies virtual-memory protection concepts to LLM agent runtimes, isolating tool-fetched content from user instructions to prevent confused-deputy attacks — an architectural defence against the root cause of indirect prompt injection.
Summary written by editorial AI · Source link below
arXiv:2610.28093v1 Announce Type: new Abstract: AI agents keep long-term memory, much of which comes from web pages and tool results. Agent harnesses, the software that runs the model, do not record who may act on this memory, so an instruction hidden in a fetched page can be treated as the user's own instruction. We propose ClawMMU, a protection layer that labels every piece of memory with its owner, scope, origins, and authority. ClawMMU checks these labels whenever content enters the model's
Editorial Analysis
Confused-deputy attacks in agentic AI systems are a growing enterprise risk; architectural isolation of data provenance is a more robust defence than content filtering alone.
Review your agentic AI frameworks for data-provenance tracking and enforce separation between user-authorised and externally-fetched content in agent memory.
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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