most citedGuardReasoner: Towards Reasoning-based LLM Safeguards

2 citations · 2 across the 19 of their papers we have counts for

collaborators

21 papers

cs.CL2026

Membership Inference in Fine-tuned Diffusion Language Models via Token-level Memorization Asymmetry

Shengfang Zhai, Leo Marchyok, Yuling Shi +4

Diffusion language models (DLMs) have recently emerged as an alternative modeling paradigm to autoregressive LMs, offering advantages such as parallel generation and bidirectional…

cs.CV2026

Robustifying Vision-Language Models via Test-Time Prompt Adaptation

Xingyu Zhu, Huanshen Wu, Shuo Wang +4

Pre-trained Vision-Language Models (VLMs) such as CLIP achieve strong zero-shot generalization, but their performance degrades sharply under adversarial perturbations. Existing tes…

cs.CR2026

BadDLM: Backdooring Diffusion Language Models with Diverse Targets

Shengfang Zhai, Xiaoyang Ji, Yuling Shi +6

Diffusion language models (DLMs) have recently emerged as an alternative modeling paradigm to autoregressive (AR) language models, enabling parallel generation and bidirectional co…

cs.CR2026

Hijacking Large Audio-Language Models via Context-Agnostic and Imperceptible Auditory Prompt Injection

Meng Chen, Kun Wang, Li Lu +2

Modern Large audio-language models (LALMs) power intelligent voice interactions by tightly integrating audio and text. This integration, however, expands the attack surface beyond…

cs.CV2026

Gym-V: A Unified Vision Environment System for Agentic Vision Research

Fanqing Meng, Lingxiao Du, Jiawei Gu +9

As agentic systems increasingly rely on reinforcement learning from verifiable rewards, standardized ``gym'' infrastructure has become essential for rapid iteration, reproducibilit…

cs.CR2026

MemPot: Defending Against Memory Extraction Attack with Optimized Honeypots

Yuhao Wang, Shengfang Zhai, Guanghao Jin +3

Large Language Model (LLM)-based agents employ external and internal memory systems to handle complex, goal-oriented tasks, yet this exposes them to severe extraction attacks, and…