3 citations · 13 across the 35 of their papers we have counts for
39 papers
CompanionSafe: From Multi-turn Safety Evaluation to Guardrails for AI Companions
Xinyang Liao, Lingyu Li, Kaicheng Shen +4
AI companions provide users with emotional support and ongoing companionship, but their sustained relational interactions also introduce distinct safety risks. Prior work largely t…
SafeAtlas-VL: Beyond Binary Multimodal Safety with Large-Scale Data and Guard Models
Zongrui Wang, Xiangyang Zhu, Sicheng Wang +13
Multimodal safety moderation requires distinguishing risks arising from visual content, user intent, and assistant behavior. Existing safeguards, however, are typically trained for…
OpenART: Scaling Agent Red Teaming via Open-Ended Environment Evolution
Yunhao Chen, Xin Wang, Yixu Wang +6
AI agents operate in persistent environments where early state changes can influence decisions far into the future. Unlike conventional language-model interactions, agent behavior…
Long-Term Simulation Exposes Cognitive-Developmental Risks in AI Companions
Kaicheng Shen, Lingyu Li, Wen Wu +3
AI companions powered by large language models increasingly interact with cognition-developing users, including children and adolescents, creating risks that may accumulate over ti…
PseudoBench: Measuring How Agentic Auto-Research Fuels Pseudoscience
Xinyang Liao, Lingyu Li, Huacan Liu +5
As Large Language Model based agents enter autonomous scientific research, their ability to resist pseudoscience becomes increasingly important. Otherwise, such systems may rapidly…
SentGuard: Sentence-Level Streaming Guardrails for Large Language Models
Jiaqi Yu, Xin Wang, Yixu Wang +4
Large language models increasingly stream long, reasoning-intensive responses in real time, making when to moderate as critical as whether to moderate. Existing guardrails fall int…
Frequency-Domain Regularized Adversarial Alignment for Transferable Attacks against Closed-Source MLLMs
Leitao Yuan, Qinghua Mao, Daizong Liu +5
Multimodal large language models (MLLMs) remain vulnerable to transfer-based targeted attacks, where perturbations optimized on open-source surrogate encoders can generalize to clo…
Towards Context-Invariant Safety Alignment for Large Language Models
Yixu Wang, Yang Yao, Xin Wang +4
Preference-based post-training aligns LLMs with human intent, yet safety behavior often remains brittle. A model may refuse a harmful request in a standard prompt but comply when t…
Safactory: A Scalable Agentic Infrastructure for Training Trustworthy Autonomous Intelligence
Xinquan Chen, Zhenyun Yin, Shan He +38
As large models evolve from conversational assistants into autonomous agents, challenges increasingly arise from long-horizon decision making, tool use, and real environment intera…
SafeSci: Safety Evaluation of Large Language Models in Science Domains and Beyond
Xiangyang Zhu, Yuan Tian, Qi Jia +14
The success of large language models (LLMs) in scientific domains has heightened safety concerns, prompting numerous benchmarks to evaluate their scientific safety. Existing benchm…
Mechanistic Origin of Moral Indifference in Language Models
Lingyu Li, Yan Teng, Yingchun Wang
Existing behavioral alignment techniques for Large Language Models (LLMs) often neglect the discrepancy between surface compliance and internal unaligned representations, leaving L…
From Sparse Decisions to Dense Reasoning: A Multi-attribute Trajectory Paradigm for Multimodal Moderation
Tianle Gu, Kexin Huang, Lingyu Li +6
Safety moderation is pivotal for identifying harmful content. Despite the success of textual safety moderation, its multimodal counterparts remain hindered by a dual sparsity of da…
OpenRT: An Open-Source Red Teaming Framework for Multimodal LLMs
Xin Wang, Yunhao Chen, Juncheng Li +7
The rapid integration of Multimodal Large Language Models (MLLMs) into critical applications is increasingly hindered by persistent safety vulnerabilities. However, existing red-te…
UniMark: Artificial Intelligence Generated Content Identification Toolkit
Meilin Li, Ji He, Yi Yu +5
The rapid proliferation of Artificial Intelligence Generated Content has precipitated a crisis of trust and urgent regulatory demands. However, existing identification tools suffer…
Evolve the Method, Not the Prompts: Evolutionary Synthesis of Jailbreak Attacks on LLMs
Yunhao Chen, Xin Wang, Juncheng Li +5
Automated red teaming frameworks for Large Language Models (LLMs) have become increasingly sophisticated, yet many still formulate attack optimization primarily in the prompt space…
MENTOR: A Metacognition-Driven Self-Evolution Framework for Uncovering and Mitigating Implicit Domain Risks in LLMs
Liang Shan, Kaicheng Shen, Wen Wu +9
Ensuring the safety of Large Language Models (LLMs) is critical for real-world deployment. However, current safety measures often fail to address implicit, domain-specific risks. T…
GhostEI-Bench: Do Mobile Agents Resilience to Environmental Injection in Dynamic On-Device Environments?
Chiyu Chen, Xinhao Song, Yunkai Chai +7
Vision-Language Models (VLMs) are increasingly deployed as autonomous agents to navigate mobile graphical user interfaces (GUIs). Operating in dynamic on-device ecosystems, which i…
Dr. Bench: A Multidimensional Evaluation for Deep Research Agents, from Answers to Reports
Yang Yao, Yixu Wang, Yuxuan Zhang +9
As an embodiment of intelligence evolution toward interconnected architectures, Deep Research Agents (DRAs) systematically exhibit the capabilities in task decomposition, cross-sou…
StolenLoRA: Exploring LoRA Extraction Attacks via Synthetic Data
Yixu Wang, Yan Teng, Yingchun Wang +1
Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA have transformed vision model adaptation, enabling the rapid deployment of customized models. However, the compactness of L…
FreezeVLA: Action-Freezing Attacks against Vision-Language-Action Models
Xin Wang, Jie Li, Zejia Weng +9
Vision-Language-Action (VLA) models are driving rapid progress in robotics by enabling agents to interpret multimodal inputs and execute complex, long-horizon tasks. However, their…
AgenticEval: Toward Agentic and Self-Evolving Safety Evaluation of Large Language Models
Yixu Wang, Xin Wang, Yang Yao +5
The rapid integration of Large Language Models (LLMs) into high-stakes domains necessitates reliable safety and compliance evaluation. However, existing static benchmarks are ill-e…
LinguaSafe: A Comprehensive Multilingual Safety Benchmark for Large Language Models
Zhiyuan Ning, Tianle Gu, Jiaxin Song +8
The widespread adoption and increasing prominence of large language models (LLMs) in global technologies necessitate a rigorous focus on ensuring their safety across a diverse rang…
SafeWork-R1: Coevolving Safety and Intelligence under the AI-45 Law
Shanghai AI Lab, :, Yicheng Bao +115
We introduce SafeWork-R1, a cutting-edge multimodal reasoning model that demonstrates the coevolution of capabilities and safety. It is developed by our proposed SafeLadder framewo…
The Other Mind: How Language Models Exhibit Human Temporal Cognition
Lingyu Li, Yang Yao, Yixu Wang +3
As Large Language Models (LLMs) continue to advance, they exhibit certain cognitive patterns similar to those of humans that are not directly specified in training data. This study…