5 papers
Is Your Prompt Poisoning Code? Defect Induction Rates and Security Mitigation Strategies
Bin Wang, YiLu Zhong, MiDi Wan +4
Large language models (LLMs) have become indispensable for automated code generation, yet the quality and security of their outputs remain a critical concern. Existing studies pred…
Multi-Agent Honeypot-Based Request-Response Context Dataset for Improved SQL Injection Detection Performance
Hao Yu, Hui Li, FengYuan Shi +4
SQL injection remains a major threat to web applications, as existing defenses often fail against obfuscation and evolving attacks because of neglecting the request-response contex…
AI Code in the Wild: Measuring Security Risks and Ecosystem Shifts of AI-Generated Code in Modern Software
Bin Wang, Wenjie Yu, Yilu Zhong +6
Large language models (LLMs) for code generation are becoming integral to modern software development, but their real-world prevalence and security impact remain poorly understood.…
Argus: A Multi-Agent Sensitive Information Leakage Detection Framework Based on Hierarchical Reference Relationships
Bin Wang, Hui Li, Liyang Zhang +5
Sensitive information leakage in code repositories has emerged as a critical security challenge. Traditional detection methods that rely on regular expressions, fingerprint feature…
MCPGuard : Automatically Detecting Vulnerabilities in MCP Servers
Bin Wang, Zexin Liu, Hao Yu +6
The Model Context Protocol (MCP) has emerged as a standardized interface enabling seamless integration between Large Language Models (LLMs) and external data sources and tools. Whi…