collaborators

11 papers

cs.AI2026

When Words Are Safe But Actions Kill: Probing Physical Jailbreak Beyond Textual Jailbreak in Hidden-State Risk Space

Weimeng Wang, Ziqiang Wang, Zihang Zhan +3

Large language models (LLMs) increasingly serve as high-level planners for embodied agents, where linguistically benign instructions can become unsafe once grounded in the physical…

cs.CR2026

AgentWard: A Lifecycle Security Architecture for Autonomous AI Agents

Yixiang Zhang, Xinhao Deng, Jiaqing Wu +3

Autonomous AI agents extend large language models into full runtime systems that load skills, ingest external content, maintain memory, plan multi-step actions, and invoke privileg…

cs.AI2026

Chain-of-Authorization: Embedding authorization into large language models

Yang Li, Yule Liu, Xinlei He +3

Although Large Language Models (LLMs) have evolved from text generators into the cognitive core of modern AI systems, their inherent lack of authorization awareness exposes these s…

cs.RO2026

Vision-Language-Action Safety: Threats, Challenges, Evaluations, and Mechanisms

Qi Li, Bo Yin, Weiqi Huang +6

Vision-Language-Action (VLA) models are emerging as a unified substrate for embodied intelligence. This shift raises a new class of safety challenges, stemming from the embodied na…

cs.CR2026

How Far Should We Need to Go : Evaluate Provenance-based Intrusion Detection Systems in Industrial Scenarios

Yue Xiao, Ling Jiang, Sen Nie +4

Provenance-based Intrusion Detection Systems (PIDSes) have been widely used to detect Advanced Persistent Threats (APTs). Although many studies achieve high performance in the eval…

cs.AI2026

LongCat-Flash-Prover: Advancing Native Formal Reasoning via Agentic Tool-Integrated Reinforcement Learning

Jianing Wang, Jianfei Zhang, Qi Guo +24

We introduce LongCat-Flash-Prover, a flagship 560-billion-parameter open-source Mixture-of- Experts (MoE) model that advances Native Formal Reasoning in Lean4 through agentic tool-…

cs.CR2026

Taming OpenClaw: Security Analysis and Mitigation of Autonomous LLM Agent Threats

Xinhao Deng, Yixiang Zhang, Jiaqing Wu +15

Autonomous Large Language Model (LLM) agents, exemplified by OpenClaw, demonstrate remarkable capabilities in executing complex, long-horizon tasks. However, their tightly coupled…

cs.AI2026

Automating Agent Hijacking via Structural Template Injection

Xinhao Deng, Jiaqing Wu, Miao Chen +3

Agent hijacking, highlighted by OWASP as a critical threat to the Large Language Model (LLM) ecosystem, enables adversaries to manipulate execution by injecting malicious instructi…

cs.CR2025

A Hard-Label Black-Box Evasion Attack against ML-based Malicious Traffic Detection Systems

Zixuan Liu, Yi Zhao, Zhuotao Liu +4

Machine Learning (ML)-based malicious traffic detection is a promising security paradigm. It outperforms rule-based traditional detection by identifying various advanced attacks. H…

cs.CR2025

Beyond a Single Perspective: Towards a Realistic Evaluation of Website Fingerprinting Attacks

Xinhao Deng, Jingyou Chen, Linxiao Yu +6

Website Fingerprinting (WF) attacks exploit patterns in encrypted traffic to infer the websites visited by users, posing a serious threat to anonymous communication systems. Althou…

cs.CR2025

Exposing LLM User Privacy via Traffic Fingerprint Analysis: A Study of Privacy Risks in LLM Agent Interactions

Yixiang Zhang, Xinhao Deng, Zhongyi Gu +4

Large Language Models (LLMs) are increasingly deployed as agents that orchestrate tasks and integrate external tools to execute complex workflows. We demonstrate that these interac…