1 citations · 1 across the 12 of their papers we have counts for
14 papers
AutoRedClaw: Automated Red-Teaming for Claw-Style Agents
Hao Li, Tongyan Hu, Weidi Luo +8
Agent systems are shifting from task-scoped tool-use agents to persistent, personalized assistants such as OpenClaw. These claw-style agents maintain configuration files (e.g., AGE…
Self-Reflection Fine-Tuning: Enhancing Agent Security against Prompt Injection Attacks from Failure Experience
Zixuan Wang, Hao Li, Fengyu Gao +5
Large language model (LLM) agents are increasingly deployed in tool-augmented environments, but their reliance on external inputs makes them highly vulnerable to prompt injection a…
ROPE: Routed Origin Policy Enforcement against Indirect Prompt Injection
Xinhang Ma, Chaowei Xiao, William Yeoh +2
Indirect prompt injection (IPI) plants instructions in the content a tool-using LLM agent reads, steering the agent into harmful tool calls. The strongest defenses are system-level…
Agent MechSuits: Mechanistic Subspace Safety Steering for Multi-Turn CLI Agents
Weidi Luo, Qiming Zhang, Yihao Quan +5
Command-Line Interface (CLI) agents based on large language models (LLMs) demonstrate remarkable autonomous capabilities, but they also introduce significant safety and misuse risk…
AutoDojo: A Generative Benchmark for Evaluating Prompt Injection Defenses in LLM Agents
Xinhang Ma, Taoran Li, Chaowei Xiao +3
Indirect prompt injection (IPI) is a major security threat to LLM-powered agents. Thus, a growing body of work has proposed a variety of defensive approaches against IPI which can…
FORTIS: Benchmarking Over-Privilege in Agent Skills
Shawn Li, Chenxiao Yu, Han Wang +8
Large language model agents increasingly operate through an intermediate skill layer that mediates between user intent and concrete task execution. This layer is widely treated as…
Runtime Skill Audit: Targeted Runtime Probing for Agent Skill Security
Tu Lan, Chaowei Xiao
Agent skills let LLM agents reuse instructions, resources, tools, and workflows, but they also create a new place for malicious behavior to hide. A skill may look benign in its doc…
ShieldNet: Network-Level Guardrails against Emerging Supply-Chain Injections in Agentic Systems
Zhuowen Yuan, Zhaorun Chen, Zhen Xiang +5
Existing research on LLM agent security mainly focuses on prompt injection and unsafe input/output behaviors. However, as agents increasingly rely on third-party tools and MCP serv…
Cooking Up Risks: Benchmarking and Reducing Food Safety Risks in Large Language Models
Weidi Luo, Xiaofei Wen, Tenghao Huang +6
Food safety directly affects human health in daily life, as unsafe preparation or storage can lead to contamination and foodborne illness. PwC's 2025 consumer survey reports that 4…
Architecting Secure AI Agents: Perspectives on System-Level Defenses Against Indirect Prompt Injection Attacks
Chong Xiang, Drew Zagieboylo, Shaona Ghosh +5
AI agents, predominantly powered by large language models (LLMs), are vulnerable to indirect prompt injection, in which malicious instructions embedded in untrusted data can trigge…
AgentSys: Secure and Dynamic LLM Agents Through Explicit Hierarchical Memory Management
Ruoyao Wen, Hao Li, Chaowei Xiao +1
Indirect prompt injection threatens LLM agents by embedding malicious instructions in external content, enabling unauthorized actions and data theft. LLM agents maintain working me…
AgentDyn: Are Your Agent Security Defenses Deployable in Real-World Dynamic Environments?
Hao Li, Ruoyao Wen, Shanghao Shi +3
AI agents that autonomously interact with external tools and environments have shown great promise across real-world applications. However, their reliance on external data exposes…
ReasAlign: Reasoning Enhanced Safety Alignment against Prompt Injection Attack
Hao Li, Yankai Yang, G. Edward Suh +2
Large Language Models (LLMs) have enabled the development of powerful agentic systems capable of automating complex workflows across various fields. However, these systems are high…
Defenses Against Prompt Attacks Learn Surface Heuristics
Shawn Li, Chenxiao Yu, Zhiyu Ni +4
Large language models (LLMs) are increasingly deployed in security-sensitive applications, where they must follow system- or developer-specified instructions that define the intend…