Jiawei Lian

Large Language Models (LLMs) increasingly employ alignment techniques to prevent harmful outputs. Despite these safeguards, attackers can circumvent them by crafting prompts that induce LLMs to generate harmful content. Current methods typically target exact affirmative responses, such as ``Sure, here is...'', suffering from limited convergence, unnatural prompts, and high computational costs. We introduce Semantic Representation Attack, a novel paradigm that fundamentally reconceptualizes adversarial objectives against aligned LLMs. Rather than targeting exact textual patterns, our approach exploits the semantic representation space comprising diverse responses with equivalent harmful meanings. This innovation resolves the inherent trade-off between attack efficacy and prompt naturalness that plagues existing methods. The Semantic Representation Heuristic Search algorithm is proposed to efficiently generate semantically coherent and concise adversarial prompts by maintaining interpretability during incremental expansion. We establish rigorous theoretical guarantees for semantic convergence and demonstrate that our method achieves unprecedented attack success rates (89.41\% averaged across 18 LLMs, including 100\% on 11 models) while maintaining stealthiness and efficiency. Comprehensive experimental results confirm the overall superiority of our Semantic Representation Attack. The code will be publicly available.

Backdoor attacks embed hidden malicious behaviors in reinforcement learning (RL) policies and activate them using triggers at test time. Most existing attacks are validated only in simulation, while their effectiveness in real-world robotic systems remains unclear. In physical deployment, safety-constrained control pipelines such as velocity limiting, action smoothing, and collision avoidance suppress abnormal actions, causing strong attenuation of conventional backdoor attacks. We study this previously overlooked problem and propose a diffusion-guided backdoor attack framework (DGBA) for real-world RL. We design small printable visual patch triggers placed on the floor and generate them using a conditional diffusion model that produces diverse patch appearances under real-world visual variations. We treat the robot control stack as a black-box system. We further introduce an advantage-based poisoning strategy that injects triggers only at decision-critical training states. We evaluate our method on a TurtleBot3 mobile robot and demonstrate reliable activation of targeted attacks while preserving normal task performance. Demo videos and code are available in the supplementary material.