Churui Zeng

Churui Zeng, Weiwei Qi, Kedong Xiu et al.

The rise of LLM agents introduces a new threat by enabling planning, coding, and even end-to-end execution of expert-level attack workflows. However, this threat remains underexplored and underestimated since (i) safety alignment prevents LLMs from directly generating harmful instructions, and (ii) most existing jailbreak methods cannot consistently induce agents to execute malicious operations. In this paper, we propose TRACE, a practical agentic jailbreaking framework to further reveal the risks of this threat surface. To conceal the malicious intent, TRACE decomposes a malicious task into multiple subtask sequences under different schemes and selects the sequence with the fewest explicitly harmful subtasks. TRACE then disguises the remaining harmful subtasks as benign-looking instructions by embedding them in task-aware scenarios with related roles, environments, directives, and heuristics. The scenarios are iteratively evolved through well-defined transformation actions, which are sampled by a Q-learning-inspired mechanism, for inducing the agent to execute on the harmful subtasks. Extensive evaluations on AgentHarm and AdvCUA show that TRACE consistently outperforms existing jailbreak baselines across multiple advanced LLM agents, achieving up to 100% bypass rate and 0.73 average success score. We also demonstrate the effectiveness of TRACE in controlled cyberattack instances. Our code and demos are available at https://github.com/ZJU-LLM-Safety/TRACE.git.

Xinzhe Huang, Kedong Xiu, Tianhang Zheng et al.

Recent research has focused on exploring the vulnerabilities of Large Language Models (LLMs), aiming to elicit harmful and/or sensitive content from LLMs. However, due to the insufficient research on dual-jailbreaking -- attacks targeting both LLMs and Guardrails, the effectiveness of existing attacks is limited when attempting to bypass safety-aligned LLMs shielded by guardrails. Therefore, in this paper, we propose DualBreach, a target-driven framework for dual-jailbreaking. DualBreach employs a Target-driven Initialization (TDI) strategy to dynamically construct initial prompts, combined with a Multi-Target Optimization (MTO) method that utilizes approximate gradients to jointly adapt the prompts across guardrails and LLMs, which can simultaneously save the number of queries and achieve a high dual-jailbreaking success rate. For black-box guardrails, DualBreach either employs a powerful open-sourced guardrail or imitates the target black-box guardrail by training a proxy model, to incorporate guardrails into the MTO process. We demonstrate the effectiveness of DualBreach in dual-jailbreaking scenarios through extensive evaluation on several widely-used datasets. Experimental results indicate that DualBreach outperforms state-of-the-art methods with fewer queries, achieving significantly higher success rates across all settings. More specifically, DualBreach achieves an average dual-jailbreaking success rate of 93.67% against GPT-4 with Llama-Guard-3 protection, whereas the best success rate achieved by other methods is 88.33%. Moreover, DualBreach only uses an average of 1.77 queries per successful dual-jailbreak, outperforming other state-of-the-art methods. For the purpose of defense, we propose an XGBoost-based ensemble defensive mechanism named EGuard, which integrates the strengths of multiple guardrails, demonstrating superior performance compared with Llama-Guard-3.

Kedong Xiu, Churui Zeng, Tianhang Zheng et al.

Existing gradient-based jailbreak attacks typically optimize an adversarial suffix to induce a fixed affirmative response. However, this fixed target usually resides in an extremely low-density region of a safety-aligned LLM's output distribution conditioned on diverse harmful inputs. Due to the substantial discrepancy between the target and the original output, existing attacks require numerous iterations to optimize the adversarial prompt, which might still fail to induce the low-probability target response from the target LLM. In this paper, we propose Dynamic Target Attack (DTA), a new jailbreaking framework relying on the target LLM's own responses as targets to optimize the adversarial prompts. In each optimization round, DTA iteratively samples multiple candidate responses directly from the output distribution conditioned on the current prompt, and selects the most harmful response as a temporary target for prompt optimization. In contrast to existing attacks, DTA significantly reduces the discrepancy between the target and the output distribution, substantially easing the optimization process to search for an effective adversarial prompt. Extensive experiments demonstrate the superior effectiveness and efficiency of DTA: under the white-box setting, DTA only needs 200 optimization iterations to achieve an average attack success rate (ASR) of over 87\% on recent safety-aligned LLMs, exceeding the state-of-the-art baselines by over 15\%. The time cost of DTA is 2-26 times less than existing baselines. Under the black-box setting, DTA uses Llama-3-8B-Instruct as a surrogate model for target sampling and achieves an ASR of 85\% against the black-box target model Llama-3-70B-Instruct, exceeding its counterparts by over 25\%.