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Boosting Jailbreak Transferability for Large Language Models
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Large language models have drawn significant attention to the challenge of safe alignment, especially regarding jailbreak attacks that circumvent security measures to produce harmful content. To address the limitations of existing methods like GCG, which perform well in single-model attacks but lack transferability, we propose several enhancements, including a scenario induction template, optimized suffix selection, and the integration of re-suffix attack mechanism to reduce inconsistent outputs. Our approach has shown superior performance in extensive experiments across various benchmarks, achieving nearly 100% success rates in both attack execution and transferability. Notably, our method has won the first place in the AISG-hosted Global Challenge for Safe and Secure LLMs. The code is released at https://github.com/HqingLiu/SI-GCG.
Forward citations
Cited by 3 Pith papers
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On Surjectivity of Neural Networks: Can you elicit any behavior from your model?
Pre-LayerNorm transformers and linear attention are almost always surjective, so any target output has an input that produces it in the continuous embedding space.
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Safety Alignment Should Be Made More Than Just A Few Attention Heads
Safety-critical attention heads are few, jailbreak prompts lower their refusal-direction signal, and fine-tuning with head-level dropout spreads safety and improves robustness.
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Stateful Cooperative Agents Safeguarding LLMs Against Evolving Multi-Turn Attacks
CoopGuard's defer-tempt-analyze-coordinate agents cut reported jailbreak success and raise attacker token costs on the new EMRA benchmark, but the deceptive-rate metric is partly defined by the paper's own scoring rubric.
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