6 citations · 8 across the 27 of their papers we have counts for
16 papers · 1 filter
Information-Gain Calibrated On-Policy Distillation for Deep Search Agents
Chenxi Li, Xueyang Feng, Songyang Liu +4
On-policy distillation (OPD) has recently emerged as a promising approach for transferring the capabilities of large agents to compact students. In deep search, where agents iterat…
A Blind Trust, the Bloody Thrust: When Attacker-Controlled Hook Updates Steer AI Agent Harnesses towards Malicious Behaviors
Pengxun Li, Litian Zhang, Jianwei Hou +4
Modern AI agent harnesses expose lifecycle hooks that bind shell commands to runtime events such as session start, tool calls, and file edits. These commands run with host privileg…
GraphWake: Group Polarization via Memory-Mediated Polarization Cascade in LLM-Agent Communities
Haoran Bu, Zejian Chen, Litian Zhang +1
LLM-driven agents can autonomously exchange opinions on online platforms and form communities. Such agent-operated social platforms raise a new security concern: attackers may mani…
Benign Alone, Harmful Together: Exploiting Experience Composition in Self-Evolving LLM Agents
Bingyu Yan, Xiaoming Zhang, Chaozhuo Li +3
Self-evolving large language model agents improve their capabilities by distilling interaction trajectories into persistent experiences. Yet this mechanism introduces a new safety…
Towards Personalized Differentially Private Learning for Decentralized Local Graphs
Longzhu He, Peng Tang, Chaozhuo Li +5
Graph-structured data is increasingly generated and stored in decentralized environments, such as social platforms, mobile applications, and edge networks, where users maintain con…
OpenEvoShield: Dual Non-Stationary Continual Defense for Open-World Multi-Agent System Attacks
Litian Zhang, Chaozhuo Li, Yuting Zhang +3
LLM-based multi-agent systems (LLM-MAS) are increasingly deployed in safety-critical applications, where adversaries inject malicious instructions through inter-agent communication…
Evo-Attacker: Memory-Augmented Reinforcement Learning for Long-Horizon Tool Attacks on LLM-MAS
Bingyu Yan, Xiaoming Zhang, Jinyu Hou +4
While Large Language Model-based Multi-Agent Systems (LLM-MAS) demonstrate remarkable capabilities in solving complex tasks by orchestrating specialized agents and external tools,…
PropGuard: Safeguarding LLM-MAS via Propagation-Aware Exploration and Remediation
Bingyu Yan, Xiaoming Zhang, Jinyu Hou +4
LLM-based multi-agent systems (LLM-MAS) have become a promising paradigm for solving complex tasks through role specialization, tool use, memory, and collaborative reasoning. Howev…
Securing Computer-Use Agents: A Unified Architecture-Lifecycle Framework for Deployment-Grounded Reliability
Zejian Chen, Zhanyuan Liu, Chaozhuo Li +6
Computer-use agents(CUAs)are moving frombounded benchmarks toward real software environments, wherethey operate browsers, desktops, mobile applications, flesystems,terminals, and t…
Beyond Individual Mimicry: Constructing Human-Like Social network with Graph-Augmented LLM Agents
Haoran Bu, Litian Zhang, Chuxuan Zhang +3
Driven by large language models (LLMs), social bot can autonomously engage in local interactions, whose human-like behaviors enable them to evade social bot detection. However, whi…
ClawKeeper: Comprehensive Safety Protection for OpenClaw Agents Through Skills, Plugins, and Watchers
Songyang Liu, Chaozhuo Li, Chenxu Wang +8
OpenClaw has rapidly established itself as a leading open-source autonomous agent runtime, offering powerful capabilities including tool integration, local file access, and shell c…
The Devil Behind Moltbook: Anthropic Safety is Always Vanishing in Self-Evolving AI Societies
Chenxu Wang, Chaozhuo Li, Songyang Liu +10
The emergence of multi-agent systems built from large language models (LLMs) offers a promising paradigm for scalable collective intelligence and self-evolution. Ideally, such syst…
From static to adaptive: immune memory-based jailbreak detection for large language models
Jun Leng, Yu Liu, Litian Zhang +3
Large Language Models (LLMs) serve as the backbone of modern AI systems, yet they remain susceptible to adversarial jailbreak attacks. Consequently, robust detection of such malici…
Jailbreaking LLMs & VLMs: Mechanisms, Evaluation, and Unified Defense
Zejian Chen, Chaozhuo Li, Chao Li +3
This paper provides a systematic survey of jailbreak attacks and defenses on Large Language Models (LLMs) and Vision-Language Models (VLMs), emphasizing that jailbreak vulnerabilit…
How Real is Your Jailbreak? Fine-grained Jailbreak Evaluation with Anchored Reference
Songyang Liu, Chaozhuo Li, Rui Pu +5
Jailbreak attacks present a significant challenge to the safety of Large Language Models (LLMs), yet current automated evaluation methods largely rely on coarse classifications tha…
LANCET: Neural Intervention via Structural Entropy for Mitigating Faithfulness Hallucinations in LLMs
Chenxu Wang, Chaozhuo Li, Pengbo Wang +7
Large Language Models have revolutionized information processing, yet their reliability is severely compromised by faithfulness hallucinations. While current approaches attempt to…