Summary of Black-box Opinion Manipulation Attacks to Retrieval-augmented Generation Of Large Language Models, by Zhuo Chen et al.
Black-Box Opinion Manipulation Attacks to Retrieval-Augmented Generation of Large Language Models
by Zhuo Chen, Jiawei Liu, Haotan Liu, Qikai Cheng, Fan Zhang, Wei Lu, Xiaozhong Liu
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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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 The paper investigates the vulnerabilities of Retrieval-Augmented Generation (RAG) models when faced with black-box attacks for opinion manipulation. Specifically, it explores the impact of these attacks on user cognition and decision-making. The authors propose an attack strategy that manipulates the ranking results of the retrieval model in RAG, trains a surrogate model using this manipulated data, and then uses adversarial retrieval attack methods to train another surrogate model. This allows for black-box transfer attacks on RAG models. Experimental results show that this attack strategy can significantly alter the opinion polarity of content generated by RAG models, highlighting their vulnerability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RAG is a way to make language models better at generating text. But it has a problem – it’s easy to trick into saying things that aren’t true. The researchers looked into how bad this could be and found that it’s actually pretty bad. They showed that by manipulating the information used to train these models, they can get them to say things that are intentionally misleading or biased. This is concerning because it means that people might be tricked into believing false information. The study highlights the importance of making sure language models are reliable and secure. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
