Summary of Multi-turn Context Jailbreak Attack on Large Language Models From First Principles, by Xiongtao Sun et al.
Multi-Turn Context Jailbreak Attack on Large Language Models From First Principles
by Xiongtao Sun, Deyue Zhang, Dongdong Yang, Quanchen Zou, Hui Li
First submitted to arxiv on: 8 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 abstract proposes a novel approach to multi-turn semantic jailbreak attacks on large language models (LLMs), which have become increasingly vulnerable to such attacks. The existing methods lack consideration for the role of multiturn dialogues in attack strategies, leading to semantic deviations during continuous interactions. To address this, the paper establishes a theoretical foundation for multi-turn attacks and proposes a context-based contextual fusion black-box jailbreak attack method called Context Fusion Attack (CFA). This method filters and extracts key terms from the target, constructs contextual scenarios around these terms, dynamically integrates the target into the scenarios, replaces malicious key terms within the target, and conceals direct malicious intent. The authors demonstrate CFA’s superiority in success rate, divergence, and harmfulness compared to other multi-turn attack strategies on various mainstream LLMs and red team datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new kind of attack on big language models that can understand and respond to many turns of conversation. Right now, these models are not very secure because attackers can easily find ways around their security measures. The researchers in this paper want to make it harder for attackers to breach the security constraints by proposing a new type of attack called Context Fusion Attack (CFA). This method works by filtering out important words from the target model, building scenarios around those words, and then incorporating the target into those scenarios. By doing this, the CFA can make it hard for the model to detect the malicious intent behind the attack. |
