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Prompt injection attacks persist despite RAG pipeline fixes

The author details their ongoing efforts to secure a Retrieval-Augmented Generation (RAG) pipeline against prompt injection attacks. After initial fixes, two readers pointed out that the pipeline's components, including embedding and reranking, needed to be tested with malicious inputs. Subsequent testing revealed that hostile chunks, even after passing through the pipeline, could still influence the model's output, leading to further patching and analysis of the model's behavior. AI

IMPACT Ongoing security research into RAG pipelines highlights the persistent challenges in preventing prompt injection attacks, even with advanced models.

RANK_REASON The item details technical fixes and ongoing testing for a specific RAG pipeline, which falls under AI tooling.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Prompt injection attacks persist despite RAG pipeline fixes

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0 / 100
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Tool
The item details technical fixes and ongoing testing for a specific RAG pipeline, which falls under AI tooling.
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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product, safety
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High
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6 days old
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Darshan kunwar ·

    Part 3: I Patched My Prompt-Injection Boundary and One Attack Still Got Through

    <p>"Two readers pushed back on my part 2 prompt-injection fix. Testing it on a real 853-chunk book, one attack broke it, a patch secured the boundary, and one attack still got through."</p> <blockquote> <p><strong>TL;DR:</strong></p> <ul> <li> <strong>Delimiters are text, not wal…