9 papers
Narrative Paradoxes in LLM Inference: Shaping Reasoning Trajectories toward Misalignment
Haoming Yang, Ke Ma, Yangbangyan Jiang +6
Explicit reasoning in large language models (LLMs) is often associated with reliability, yet it can still pose risks when a model infers harmful goals under a benign context. We id…
Technical Report on the CVPR 2026@AdvML Workshop Challenge
Tianyuan Zhang, Zonglei Jing, Jiangfan Liu +47
Vision-language agents (VLAs) are increasingly used to interpret complex driving scenes and support safety-critical reasoning. This report presents the CVPR 2026@AdvML Workshop Cha…
Localize and Neutralize: Gradient-guided Token Suppression against Visual Prompt Injection Attack
Dongpeng Zhang, Ke Ma, Yangbangyan Jiang +4
Adversarial images pose a severe security threat to multimodal large language models through prompt injection. Existing defenses largely lack a principled understanding of the unde…
Learn Faster and Remember More: Balancing Exploration and Exploitation for Continual Test-time Adaptation
Pinci Yang, Peisong Wen, Ke Ma +1
Continual Test-Time Adaptation (CTTA) aims to adapt a source pre-trained model to continually changing target domains during inference. As a fundamental principle, an ideal CTTA me…
Pushing the Limits of Safety: A Technical Report on the ATLAS Challenge 2025
Zonghao Ying, Siyang Wu, Run Hao +44
Multimodal Large Language Models (MLLMs) have enabled transformative advancements across diverse applications but remain susceptible to safety threats, especially jailbreak attacks…
A Unified Framework for Stealthy Adversarial Generation via Latent Optimization and Transferability Enhancement
Gaozheng Pei, Ke Ma, Dongpeng Zhang +3
Due to their powerful image generation capabilities, diffusion-based adversarial example generation methods through image editing are rapidly gaining popularity. However, due to re…
Cannot See the Forest for the Trees: Invoking Heuristics and Biases to Elicit Irrational Choices of LLMs
Haoming Yang, Ke Ma, Xiaojun Jia +3
Despite the remarkable performance of Large Language Models (LLMs), they remain vulnerable to jailbreak attacks, which can compromise their safety mechanisms. Existing studies ofte…
Diffusion-based Adversarial Purification from the Perspective of the Frequency Domain
Gaozheng Pei, Ke Ma, Yingfei Sun +2
The diffusion-based adversarial purification methods attempt to drown adversarial perturbations into a part of isotropic noise through the forward process, and then recover the cle…
Exploring Query Efficient Data Generation towards Data-free Model Stealing in Hard Label Setting
Gaozheng Pei, Shaojie lyu, Ke Ma +3
Data-free model stealing involves replicating the functionality of a target model into a substitute model without accessing the target model's structure, parameters, or training da…