Decision Models as Retrieval Gates Against Injection and Pollution
Open-weight decision models that classify without generating tokens are tested as pre-retrieval filters against prompt injection and data pollution in RAG pipelines—a practical defence layer for enterprises adopting agentic AI.
Summary written by editorial AI · Source link below
arXiv:2611.08325v1 Announce Type: new Abstract: Retrieved web text can carry instructions aimed at the language model, or synthetic pages written to mislead. We test whether released open-weight decision models, which answer typed questions about a text without generating tokens, can screen retrieved passages against both threats. On a benchmark of 1,000 questions over forum-style web pages with hidden injected instructions, a Kev-4B gate reads each passage's HTML. Attack success, measured auto
Editorial Analysis
As enterprises embed RAG-based AI assistants, unscreened retrieved content becomes an injection vector; decision-model gates could reduce this attack surface without heavy computational cost.
Assess whether your RAG or agentic AI workflows include a pre-retrieval content-screening step and evaluate decision-model approaches for that role.
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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