Three Heads Are Better Than One: A Multi-Perspective Reasoning Framework for Enhanced Vulnerability Detection

Automated vulnerability detection is crucial for enhancing software security by identifying potential flaws that attackers could exploit, thereby reducing the reliance on labor-intensive manual code audits. Recent advancements have shifted towards leveraging large language models LLMs for...

VulKey: Automated Vulnerability Repair Guided by Domain-Specific Repair Patterns

The increasing prevalence of software vulnerabilities highlights the need for effective Automatic Vulnerability Repair AVR tools. While LLM-based approaches are promising, they struggle to incorporate structured security knowledge from sources like CWE and NVD. Current methods either use this...

AEGIS: From Clues to Verdicts -- Graph-Guided Deep Vulnerability Reasoning Via Dialectics and Meta-Auditing

Large Language Models LLMs are increasingly adopted for vulnerability detection, yet their reasoning remains fundamentally unsound. We identify a root cause shared by both major mitigation paradigms agent-based debate and retrieval augmentation: reasoning in an ungrounded deliberative space that...