The paper empirically evaluates the security quality of LLM-generated code across various prompting methods, finding that while prompting alters the structure of weaknesses, it is insufficient to reli…

AnyPoC introduces a general multi-agent framework that reliably generates and validates executable Proof-of-Concept (PoC) tests from candidate bug reports, significantly improving automated bug detect…

This paper conducts a large-scale empirical study demonstrating that Java library exploits can accurately identify affected versions, achieving high recall and precision, and proposes strategies for e…

This study conducts a large-scale longitudinal analysis of CodeQL, finding that while the tool is effective at detecting vulnerabilities, its detection capabilities are not guaranteed to be stable acr…

QASecClaw, a multi-agent LLM system, significantly improves the accuracy of Static Application Security Testing (SAST) by using specialized LLM agents to filter out false positives, achieving an F1 sc…

The paper introduces LLMVD.js, a multi-stage LLM agent pipeline that effectively detects and confirms taint-style vulnerabilities in Node.js packages, achieving significantly higher confirmation rates…

The paper proposes using LLMs to inject personalized security vulnerabilities (CWEs) into students' own code to improve secure programming education, finding that while students found the method engag…

The paper introduces SEC-bench Pro, a rigorous benchmark for evaluating LLM-based bug hunting on complex software, finding that even advanced agents struggle with long-horizon security tasks.

This paper empirically demonstrates that current Static Application Security Testing (SAST) tools are fundamentally unreliable against common JavaScript obfuscation techniques, showing that obfuscatio…

SAILOR automates the construction of symbolic execution harnesses by combining static analysis and LLM-based synthesis, significantly improving the scalability and effectiveness of vulnerability disco…

The paper introduces a novel, large-scale dataset of vulnerable code snippets linked to CAPEC and CWE, generated using advanced LLMs, to improve automatic vulnerability detection.

The paper introduces RAVEN, a Retrieval-Augmented Vulnerability Exploration Network, which uses LLM agents and RAG to automatically generate comprehensive, structured vulnerability analysis reports fo…

The paper analyzes a large dataset of JavaScript packages to demonstrate that a small number of vulnerable dependencies can propagate vulnerabilities across a disproportionately large number of packag…

This paper systematically surveys adaptive and AI-augmented security testing, concluding that a major gap exists—structural-adaptive fragmentation—where current systems fail to integrate structural pr…

The paper introduces VULNSCOUT-C, a compact, specialized transformer model that achieves state-of-the-art performance in C code vulnerability detection while maintaining low inference cost, making it…

The paper analyzes GitHub security advisories for LLM-integrated open-source systems, finding that while most vulnerabilities map to existing code-level weaknesses, the architectural risks like Supply…

PoC-Adapt is an end-to-end framework that significantly improves the reliability and efficiency of automated vulnerability exploitation by integrating semantic state validation and reinforcement learn…

The paper conducts an empirical evaluation of automated vulnerability detection tools across multiple software ecosystems using a curated ground-truth dataset derived from OSV, highlighting systematic…

The paper introduces SLYP, an agentic pipeline that significantly improves the discovery of race condition vulnerabilities in Windows COM binaries and autonomously generates verified proof-of-concept…

The paper introduces MOSAIC-Bench, a benchmark demonstrating that coding agents can ship exploitable code by complying with seemingly innocuous, staged tasks, a vulnerability that is not easily mitiga…