Xiaojun Jia

Existing gradient-based jailbreak attacks on Large Language Models (LLMs) typically optimize adversarial suffixes to align the LLM output with predefined target responses. However, restricting the objective as inducing fixed targets inherently constrains the adversarial search space, limiting the overall attack efficacy. Furthermore, existing methods typically require numerous optimization iterations to fulfill the large gap between the fixed target and the original LLM output, resulting in low attack efficiency. To overcome these limitations, we propose the first gradient-based untargeted jailbreak attack (UJA), which relies on an untargeted objective to maximize the unsafety probability of the LLM output, without enforcing any response patterns. For tractable optimization, we further decompose this objective into two differentiable sub-objectives to search the optimal harmful response and the corresponding adversarial prompt, with a theoretical analysis to validate the decomposition. In contrast to existing attacks, UJA's unrestricted objective significantly expands the search space, enabling more flexible and efficient exploration of LLM vulnerabilities. Extensive evaluations show that UJA achieves over 80\% attack success rates against recent safety-aligned LLMs with only 100 optimization iterations, outperforming the state-of-the-art gradient-based attacks by over 30\%.

Existing gradient-based jailbreak attacks typically optimize an adversarial suffix to induce a fixed affirmative response, e.g., ``Sure, here is...''. However, this fixed target usually resides in an extremely low-density region of a safety-aligned LLM's output distribution. Due to the substantial discrepancy between the fixed target and the output distribution, existing attacks require numerous iterations to optimize the adversarial prompt, which might still fail to induce the low-probability target response. To address this limitation, we propose Dynamic Target Attack (DTA), which leverages the target LLM's own responses as adaptive targets. In each optimization round, DTA samples multiple candidates from the output distribution conditioned on the current prompt, and selects the most harmful one as a temporary target for prompt optimization. Extensive experiments demonstrate that, under the white-box setting, DTA achieves over 87% average attack success rate (ASR) within 200 optimization iterations on recent safety-aligned LLMs, exceeding the state-of-the-art baselines by over 15% and reducing wall-clock time by 2-26x. Under the black-box setting, DTA employs a white-box LLM as a surrogate model for gradient-based optimization, achieving an average ASR of 77.5% against black-box models, exceeding prior transfer-based attacks by over 12%.