6 papers
Inverting the Shield: Systematically Generating Safety Tests from Policy Specifications
Xiaoyue Lu, Xianglin Yang, Haijun Liu +4
The widespread integration of Large Language Models (LLMs) necessitates rigorous and systematic safety evaluation. Existing paradigms either rely on constructed benchmarks to asses…
ThreatPilot: Attack-Driven Threat Intelligence Extraction
Ming Xu, Hongtai Wang, Jiahao Liu +6
Efficient defense against dynamically evolving advanced persistent threats (APT) requires the structured threat intelligence feeds, such as techniques used. However, existing threa…
VULSOLVER: Vulnerability Detection via LLM-Driven Constraint Solving
Xiang Li, Yueci Su, Jiahao Liu +4
Traditional vulnerability detection methods rely heavily on predefined rule matching, which often fails to capture vulnerabilities accurately. With the rise of large language model…
TraceAegis: Securing LLM-Based Agents via Hierarchical and Behavioral Anomaly Detection
Jiahao Liu, Bonan Ruan, Xianglin Yang +5
LLM-based agents have demonstrated promising adaptability in real-world applications. However, these agents remain vulnerable to a wide range of attacks, such as tool poisoning and…
Towards Scalable and Interpretable Mobile App Risk Analysis via Large Language Models
Yu Yang, Zhenyuan Li, Xiandong Ran +4
Mobile application marketplaces are responsible for vetting apps to identify and mitigate security risks. Current vetting processes are labor-intensive, relying on manual analysis…
A Large-Scale Evolvable Dataset for Model Context Protocol Ecosystem and Security Analysis
Zhiwei Lin, Bonan Ruan, Jiahao Liu +1
The Model Context Protocol (MCP) has recently emerged as a standardized interface for connecting language models with external tools and data. As the ecosystem rapidly expands, the…