Compaction Laundering: Provenance Destruction in Context-Compacting Coding Agents
Researchers show that popular coding agents' context-compaction step destroys provenance metadata, letting injected instructions survive as trusted memory — a new supply-chain vector for AI-assisted development.
Summary written by editorial AI · Source link below
arXiv:2610.29869v1 Announce Type: new Abstract: Coding agents that run long enough must compact their context, summarizing the conversation, discarding the original, and carrying the summary forward as memory. We show that in popular open-source coding agents this step is an exploitable trust boundary. Harnesses that flatten the typed conversation into a single plain string, marking each message with a text label such as [User]:, destroy message provenance in code, before the summarizer runs. A
Editorial Analysis
Enterprises adopting AI coding assistants face a hidden trust boundary: summarisation can launder malicious injections into trusted context, bypassing existing code-review controls.
Review all AI coding-agent deployments for context-compaction behaviour and mandate independent verification of agent-generated code provenance.
AI coding assistants may unknowingly carry forward attacker-injected instructions through a newly discovered trust gap in their memory management.
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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