Adding a Trust Boundary to a LlamaIndex RAG Pipeline
The article discusses the importance of implementing a trust boundary in a LlamaIndex RAG pipeline to prevent untrusted content from external documents influencing model behavior. Retrieved text, such as from PDFs or emails, can contain hidden instructions that compromise security if treated the same as useful evidence. The proposed solution involves placing a trust boundary between retrieval and response synthesis to filter and control what content becomes part of the model's context.
- ▪Retrieved documents in a RAG pipeline can contain both useful evidence and harmful instructions.
- ▪The trust boundary should be placed between retrieval and synthesis to prevent untrusted text from shaping model context.
- ▪Omega Walls can enforce this boundary by filtering untrusted content and allowing only safe chunks into the context.
- ▪System policies and user requests are considered trusted, while retrieved files and tool outputs are treated as untrusted.
- ▪Post-generation checks are insufficient because harmful influence occurs during context construction, not in the final output.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3807721) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Anton Fedotov Posted on Apr 29 Adding a Trust Boundary to a LlamaIndex RAG Pipeline #llamaindex #agents #security #rag Your LlamaIndex app does not only retrieve documents. It decides which external text is allowed to become model context. That is a trust decision, even if your code does not call it one. A PDF can contain useful facts. A support ticket can contain real customer context. A web page can contain documentation. An email thread can contain the answer your user needs.
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