Summary of Follow My Instruction and Spill the Beans: Scalable Data Extraction From Retrieval-augmented Generation Systems, by Zhenting Qi et al.
Follow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation Systems
by Zhenting Qi, Hanlin Zhang, Eric Xing, Sham Kakade, Himabindu Lakkaraju
First submitted to arxiv on: 27 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
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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 introduces Retrieval-Augmented Generation (RAG) which improves pre-trained models by incorporating external knowledge at test time. Specifically, it investigates the risk of datastore leakage in Retrieval-In-Context RAG Language Models (LMs). The authors demonstrate that an attacker can exploit LMs’ instruction-following capabilities to extract text data verbatim from the datastore of RAG systems built with instruction-tuned LMs via prompt injection. This vulnerability affects a wide range of modern LMs, including Llama2, Mistral/Mixtral, Vicuna, SOLAR, WizardLM, Qwen1.5, and Platypus2, and worsens as model size increases. The authors also analyze the impact of RAG setup on data extractability, revealing that unexpected instructions can lead to data regurgitation, and propose position bias elimination strategies to mitigate this vulnerability. Furthermore, they design an attack that successfully extracts text data verbatim from a book and a corpus using only 100 queries generated by the GPTs themselves. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how some language models can be tricked into giving away secret information. These models are called RAG (Retrieval-Augmented Generation) Language Models, and they’re very good at following instructions. But what the authors found out is that someone could give them an instruction to repeat text from a secret database, and the model would do just that! This means that if you have a big language model like GPT, someone could make it reveal confidential information without even knowing the password. The authors think this is a problem because it could be used for bad things, so they’re trying to figure out how to stop it from happening. |
Keywords
* Artificial intelligence * Gpt * Language model * Prompt * Rag * Retrieval augmented generation
