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Don't Say No: Jailbreaking LLM by Suppressing Refusal
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Ensuring the safety alignment of Large Language Models (LLMs) is critical for generating responses consistent with human values. However, LLMs remain vulnerable to jailbreaking attacks, where carefully crafted prompts manipulate them into producing toxic content. One category of such attacks reformulates the task as an optimization problem, aiming to elicit affirmative responses from the LLM. However, these methods heavily rely on predefined objectionable behaviors, limiting their effectiveness and adaptability to diverse harmful queries. In this study, we first identify why the vanilla target loss is suboptimal and then propose enhancements to the loss objective. We introduce DSN (Don't Say No) attack, which combines a cosine decay schedule method with refusal suppression to achieve higher success rates. Extensive experiments demonstrate that DSN outperforms baseline attacks and achieves state-of-the-art attack success rates (ASR). DSN also shows strong universality and transferability to unseen datasets and black-box models.
Forward citations
Cited by 7 Pith papers
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SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses
A systemization of LLM jailbreak security that adds linked taxonomies, an evaluation platform, and JailbreakDB, while its main attack–defense comparison results remain deferred.
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JADES: A Universal Framework for Jailbreak Assessment via Decompositional Scoring
JADES judges jailbreak success by decomposing harmful prompts into weighted sub-questions and scoring each part, claiming 98.5% human agreement and showing prior attack success rates are inflated.
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Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation
Sparse autoencoder activation perturbation (SFPF) applied on top of existing jailbreak prompts raises attack success rate on Qwen3-32B, but with no defense evaluation and weak reproducibility.
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Beyond Jailbreaks: Revealing Stealthier and Broader LLM Security Risks Stemming from Alignment Failures
JailFlipBench and JailFlip attacks show that leading LLMs can be made to answer benign-looking questions with plausible but factually wrong and dangerous responses.
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SoK: A Comprehensive Security Analysis of Jailbreak Resilience in GPT and DeepSeek Models
Across 510 HarmBench behaviors and seven attack methods, GPT-4 models show more consistent jailbreak resilience than DeepSeek models, whose vulnerability grows with scale.
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From Rogue to Safe AI: The Role of Explicit Refusals in Aligning LLMs with International Humanitarian Law
Across eight LLMs, most explicitly IHL-violating prompts are refused, and a single system-level safety prompt raises explanatory refusal rates in six of eight models, though the benchmark is not publicly released.
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Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages
Relabeling hate speech as metaphor pairs (red/green, summer/winter) in prompts raises Llama2's F1 on a 500-item Bengali subsample to 95.89, though the gain is reported without matched test-set comparisons or error bars.
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