Memory Provenance Laundering in LLM Agents: A Non-Amplification Firewall for Persistent Memory

Abstract:Long-term memory lets large language model(LLM) agents reuse prior preferences and work flows, but it also turns untrusted observations into persistent action context. We identify memory provenance laundering: during LLM-based memory consolidation, an external observation may be rewritten as apparent user history or workflow support, preserving an action trigger while erasing the low-trust source that should limit its authority. Existing prompt filters, content sanitizers, and tool guards do not enforce source-authority non-amplification after lossy memory consolidation. We formalize this boundary and instantiate it as Provenance-Preserving Memory Fire wall (PPMF), a lightweight memory middleware that preserves platform-maintained provenance and authorizes tool calls by matching action risk to the authority of action-relevant memories. In our schema-grounded evaluation with fixed risk policies, vulnerable consolidated memories reach up to 1.000 attack success rate(ASR); with intact platform-maintained provenance, confirmation, and risk labels, no evaluated unauthorized high-risk action passes the PPMF gate while confirmed benign actions and targeted low-risk memory use remain executable.

Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)

arXiv:2607.29167 [cs.CR]

(or arXiv:2607.29167v1 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2607.29167 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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