Vulnerability Detection
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Benchmarks
VulScribeR
Most implemented
NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security
VulDeePecker: A Deep Learning-Based System for Vulnerability Detection
SAFE: Self-Attentive Function Embeddings for Binary Similarity
ARQ: Agentic CodeQL Query Refinement for C/C++ Vulnerability Detection
Papers
Large Language Models at the Intersection of Software Engineering and Software Security:An Evidence-Centered Structured Survey and Research Agenda
Large Language Models (LLMs) are moving from code completion toward repository-scale agents that retrieve context, edit files, execute tools, and participate in security-sensitive workflows. The evidence for these system…
Vulnerability DetectionCode CompletionARQ: Agentic CodeQL Query Refinement for C/C++ Vulnerability Detection
Static analyzers have been widely adopted for vulnerability detection in C/C++ programs. Query-based static analyzers (e.g., CodeQL) encode vulnerable code patterns in detection queries and match them against source code…
Vulnerability DetectionFrequency-Aware Continual Learning for Smart Contract Vulnerability Detection with Large Language Models
Smart contract vulnerability detection with Large Language Models (LLMs) faces three causally linked challenges. First, new vulnerability categories demand parameter-efficient adaptation, since full retraining is prohibi…
Vulnerability DetectionContinual LearningTRUSS: Towards Task-Reliable and User-Safe Automated Agent Skill Generation
Agent Skills package reusable natural language procedures with executable resources, enabling software agents to acquire task specific capabilities without model adaptation. Automatically generating such Skills can impro…
Vulnerability DetectionSecuring AI-Generated Code: A Just-in-Time Vulnerability Detection and Remediation Pipeline
AI-assisted development tools generate vulnerable code at significant rates, yet few automated mechanisms exist to detect, enrich, fix, and verify security issues at development velocity, particularly ones that ground re…
Vulnerability DetectionCode GenerationGraph Is the Verifier: Agentic Reinforcement Learning for Interprocedural Vulnerability Detection
Real-world vulnerabilities often span multiple functions, yet most learning-based detectors classify each function in isolation: on a sample of real CVEs, we find that 71.7% of vulnerable functions require evidence from …
Vulnerability DetectionReinforcement Learning