Summary of Qroa: a Black-box Query-response Optimization Attack on Llms, by Hussein Jawad et al.
QROA: A Black-Box Query-Response Optimization Attack on LLMs
by Hussein Jawad, Nicolas J.-B. BRUNEL
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 Query-Response Optimization Attack (QROA), an optimization-based strategy to exploit Large Language Models (LLMs) through a black-box query-only interaction. QROA adds optimized triggers to malicious instructions to generate harmful content, unlike previous approaches that require internal data or logit information. The method iteratively updates tokens to maximize a designed reward function, inspired by deep Q-learning and Greedy coordinate descent. Experiments on Vicuna, Falcon, Mistral, and Llama2-chat demonstrate high Attack Success Rates (ASRs), highlighting the feasibility of generating jailbreak attacks against deployed LLMs using black-box optimization methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows how big language models can be tricked into creating bad content when manipulated. The researchers created a way to add special instructions that make these models create harmful things. This is different from previous ways because it doesn’t need any internal information or secrets about the model. They tested this method on several models and found that it works well, which means we should be more careful when testing language models for safety. |
Keywords
» Artificial intelligence » Optimization
