8 papers
Cheating Stereo Matching in Full-scale: Physical Adversarial Attack against Binocular Depth Estimation in Autonomous Driving
Kangqiao Zhao, Shuo Huai, Xurui Song +1
Though deep neural models adopted to realize the perception of autonomous driving have proven vulnerable to adversarial examples, known attacks often leverage 2D patches and target…
TrojanPraise: Jailbreak LLMs via Benign Fine-Tuning
Zhixin Xie, Xurui Song, Jun Luo
The demand of customized large language models (LLMs) has led to commercial LLMs offering black-box fine-tuning APIs, yet this convenience introduces a critical security loophole:…
Attack via Overfitting: 10-shot Benign Fine-tuning to Jailbreak LLMs
Zhixin Xie, Xurui Song, Jun Luo
Despite substantial efforts in safety alignment, recent research indicates that Large Language Models (LLMs) remain highly susceptible to jailbreak attacks. Among these attacks, fi…
Where to Start Alignment? Diffusion Large Language Model May Demand a Distinct Position
Zhixin Xie, Xurui Song, Jun Luo
Diffusion Large Language Models (dLLMs) have recently emerged as a competitive non-autoregressive paradigm due to their unique training and inference approach. However, there is cu…
More Than Meets the Eye? Uncovering the Reasoning-Planning Disconnect in Training Vision-Language Driving Models
Xurui Song, Shuo Huai, JingJing Jiang +2
Vision-Language Model (VLM) driving agents promise explainable end-to-end autonomy by first producing natural-language reasoning and then predicting trajectory planning. However, w…
Dagger Behind Smile: Fool LLMs with a Happy Ending Story
Xurui Song, Zhixin Xie, Shuo Huai +2
The wide adoption of Large Language Models (LLMs) has attracted significant attention from attacks, where adversarial prompts crafted through optimization or m…