Summary of Pirates Of the Rag: Adaptively Attacking Llms to Leak Knowledge Bases, by Christian Di Maio et al.
Pirates of the RAG: Adaptively Attacking LLMs to Leak Knowledge Bases
by Christian Di Maio, Cristian Cosci, Marco Maggini, Valentina Poggioni, Stefano Melacci
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel black-box attack on Retrieval-Augmented Generation (RAG) systems that can force them to leak their private knowledge bases, which are used to improve the generative capabilities of Large Language Models (LLMs). The proposed algorithm uses a relevance-based mechanism and an attacker-side open-source LLM to generate effective queries that leak most of the hidden knowledge base. The authors demonstrate the effectiveness of this attack through extensive experimentation on different RAG pipelines and domains, comparing it to recent related approaches. The findings highlight the urgent need for more robust privacy safeguards in the design and deployment of RAG systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to hack big language models that help computers understand human language better. These models are like super smart librarians who can give you answers based on what they know, but sometimes they keep secrets too. The hackers want to get these secrets out, so they created a special tool that asks questions in the right way to make the model reveal its hidden knowledge. They tested this tool and it worked well, even compared to other tools that tried to do the same thing. This means we need to be more careful when building these models so people’s private information stays safe. |
Keywords
» Artificial intelligence » Knowledge base » Rag » Retrieval augmented generation
