3 citations · 7 across the 24 of their papers we have counts for
28 papers
CyberPersistBench: Evaluating LLM-Based Cyber Attackers on Installation and Persistence
Sujin Chen, Lijun Li, Xuhong Wang +1
While LLM-based attackers exhibit growing proficiency in vulnerability exploitation, most existing cybersecurity benchmarks suffer from single-stage truncation, prematurely termina…
MobileWorldSafety: Benchmarking GUI Agent Safety Against Environmental Injection Attacks in Android Apps
Sujin Chen, Lijun Li, Tianyi Du +1
LLM-powered GUI agents that autonomously operate smartphones are rapidly transitioning from research prototypes to early real-world deployment. However, because these agents routin…
An Early Warning of Emerging Biosecurity Risks in Frontier LLMs
Zhida He, Xia Hu, Baichen Le +20
Frontier large language models (LLMs) are increasingly integrated into scientific workflows, yet their growing biological capabilities may outpace current safeguards. To assess the…
Is Deep Research Reliable? Misleading Knowledge Induces False Conclusions
Pengyu Zhu, Lijun Li, Longju Yang +2
Deep Research agents conduct long-horizon investigations by iteratively planning, retrieving evidence, and generating reports. However, it remains unclear whether they can resist a…
DeepSafe-SciHazard: A Benchmark for Measuring Scientific Safety Risks with Decomposed Harm Scoring
Chunxiao Li, Yuan Xiong, Lijun Li +4
Large language models (LLMs) increasingly support science, but they can also convert hazardous scientific knowledge into actionable misuse guidance. Existing benchmarks often rely…
AgentSchool: An LLM-Powered Multi-Agent Simulation for Education
Yulei Ye, Wenhao Li, Zhong Wen +23
Despite the rapid deployment of LLMs into classrooms, validating educational AI remains uniquely intractable: interventions act on developing learners whose cognitive and social tr…
UniACE: A Unified Framework for Evaluating LLM Agentic Capabilities
Pengyu Zhu, Lijun Li, Yaxing Lyu +8
Agent benchmarks are increasingly used to compare large language models (LLMs) across domains, yet a reported score reflects a complete model--harness--environment configuration ra…
SEARL: Joint Optimization of Policy and Tool Graph Memory for Self-Evolving Agents
Xinshun Feng, Xinhao Song, Lijun Li +2
Recent advances in Reinforcement Learning with Verifiable Rewards (RLVR) have demonstrated significant potential in single-turn reasoning tasks. With the paradigm shift toward self…
TreeTeaming: Autonomous Red-Teaming of Vision-Language Models via Hierarchical Strategy Exploration
Chunxiao Li, Lijun Li, Jing Shao
The rapid advancement of Vision-Language Models (VLMs) has brought their safety vulnerabilities into sharp focus. However, existing red teaming methods are fundamentally constraine…
ToolSafe: Enhancing Tool Invocation Safety of LLM-based agents via Proactive Step-level Guardrail and Feedback
Yutao Mou, Zhangchi Xue, Lijun Li +4
While LLM-based agents can interact with environments via invoking external tools, their expanded capabilities also amplify security risks. Monitoring step-level tool invocation be…
ProGuard: Towards Proactive Multimodal Safeguard
Shaohan Yu, Lijun Li, Chenyang Si +2
The rapid evolution of generative models has led to a continuous emergence of multimodal safety risks, exposing the limitations of existing defense methods. To address these challe…
Contextual Image Attack: How Visual Context Exposes Multimodal Safety Vulnerabilities
Yuan Xiong, Ziqi Miao, Lijun Li +3
While Multimodal Large Language Models (MLLMs) show remarkable capabilities, their safety alignments are susceptible to jailbreak attacks. Existing attack methods typically focus o…
Collaborative Shadows: Distributed Backdoor Attacks in LLM-Based Multi-Agent Systems
Pengyu Zhu, Lijun Li, Yaxing Lyu +3
LLM-based multi-agent systems (MAS) demonstrate increasing integration into next-generation applications, but their safety in backdoor attacks remains largely underexplored. Howeve…
HarmRLVR: Weaponizing Verifiable Rewards for Harmful LLM Alignment
Yuexiao Liu, Lijun Li, Xingjun Wang +1
Recent advancements in Reinforcement Learning with Verifiable Rewards (RLVR) have gained significant attention due to their objective and verifiable reward signals, demonstrating s…
The Plot Twist: Jailbreaking Unified Multimodal Models with a Three-Act NarrativeAttack
Shaoxiong Guo, Tianyi Du, Lijun Li +3
Unified Multimodal Understanding and Generation Models (UMMs) increasingly combine visual understanding and image generation within a single interactive workflow, making generated…
Self-adaptive Dataset Construction for Real-World Multimodal Safety Scenarios
Jingen Qu, Lijun Li, Bo Zhang +2
Multimodal large language models (MLLMs) are rapidly evolving, presenting increasingly complex safety challenges. However, current dataset construction methods, which are risk-orie…
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…
Layer-Aware Representation Filtering: Purifying Finetuning Data to Preserve LLM Safety Alignment
Hao Li, Lijun Li, Zhenghao Lu +4
With rapid advancement and increasing accessibility of LLMs, fine-tuning aligned models has become a critical step for adapting them to real-world applications, which makes the saf…
Response Attack: Exploiting Contextual Priming to Jailbreak Large Language Models
Ziqi Miao, Lijun Li, Yuan Xiong +3
Contextual priming, where earlier stimuli covertly bias later judgments, offers an unexplored attack surface for large language models (LLMs). We uncover a contextual priming vulne…
Visual Contextual Attack: Jailbreaking MLLMs with Image-Driven Context Injection
Ziqi Miao, Yi Ding, Lijun Li +1
With the emergence of strong vision language capabilities, multimodal large language models (MLLMs) have demonstrated tremendous potential for real-world applications. However, the…
Evolutionary Guided Decoding: Iterative Value Refinement for LLMs
Zhenhua Liu, Lijun Li, Ruizhe Chen +5
While guided decoding, especially value-guided methods, has emerged as a cost-effective alternative for controlling language model outputs without re-training models, its effective…
Rethinking Bottlenecks in Safety Fine-Tuning of Vision Language Models
Yi Ding, Lijun Li, Bing Cao +1
Large Vision-Language Models (VLMs) have achieved remarkable performance across a wide range of tasks. However, their deployment in safety-critical domains poses significant challe…
T2ISafety: Benchmark for Assessing Fairness, Toxicity, and Privacy in Image Generation
Lijun Li, Zhelun Shi, Xuhao Hu +5
Text-to-image (T2I) models have rapidly advanced, enabling the generation of high-quality images from text prompts across various domains. However, these models present notable saf…
WorldSimBench: Towards Video Generation Models as World Simulators
Yiran Qin, Zhelun Shi, Jiwen Yu +10
Recent advancements in predictive models have demonstrated exceptional capabilities in predicting the future state of objects and scenes. However, the lack of categorization based…