The paper proposes agentic fuzzing, a novel bug-finding approach where deep agents perform direct reasoning based on historical bugs to discover logic bugs in mature codebases.

SDLLMFuzz is a novel dynamic-static framework that combines LLM-based structure-aware input generation with semantic feedback from crash analysis to significantly improve vulnerability discovery in st…

FuzzAgent introduces a multi-agent, evolutionary system that significantly improves library fuzzing by iteratively refining the test suite based on runtime feedback, achieving superior coverage and bu…

The paper addresses the gap in understanding real-world LLM-in-the-loop vulnerabilities by creating the LLMCVE dataset and demonstrating that these vulnerabilities are significantly harder to repair t…

The paper proposes LiveFuzz, a directed greybox fuzzing technique that detects the exploitability of third-party library vulnerabilities from client programs without requiring pre-existing proof-of-co…

The paper introduces a novel multi-LLM orchestration system combined with symbolic execution to successfully detect memory vulnerabilities in uncompilable, incomplete Rust CVE code snippets, achieving…

The paper introduces AgentFlow, a novel framework that uses a typed graph DSL and feedback-driven optimization to automatically synthesize and improve multi-agent harnesses for discovering security vu…

FunFuzz introduces a multi-island evolutionary fuzzing framework that uses LLMs to generate structured inputs, achieving superior compiler coverage and discovering more unique failures compared to exi…

This paper measures the prevalence of recurring vulnerability patterns (variants) across multiple AI infrastructure repositories and proposes INFRASCOPE, a framework to automatically detect these vari…

The paper introduces Patch2Vuln, a pipeline that uses an LLM agent to reconstruct security vulnerabilities by analyzing differences between old and new Linux binary packages, successfully localizing p…

AgenticVM is a multi-agent framework that uses LLMs and specialized tools to automate and drastically reduce the volume of software vulnerabilities into actionable, prioritized queues.

VIPER-MCP is a novel, end-to-end automated framework that detects and dynamically confirms the exploitability of taint-style vulnerabilities in Model Context Protocol (MCP) servers, achieving high-fid…

TitanCA presents a novel, multi-agent LLM orchestration framework that significantly improves vulnerability discovery by reducing false positives and identifying numerous zero-day vulnerabilities.

The paper introduces GRIEF, a greybox fuzzer that discovers critical, concurrency-related vulnerabilities in LLM serving systems by treating timed multi-request traces as inputs, finding issues like c…

AFL-ICP is a novel specification-driven fuzzing framework that significantly enhances the security testing of industrial control protocols by detecting subtle semantic and logic bugs missed by traditi…

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 VCAO, a novel verifier-centered agentic orchestration framework that models OS vulnerability discovery as a Bayesian Stackelberg game, significantly improving vulnerability discov…

The paper proposes a novel '3+1' heterogeneous multi-agent architecture using cloud LLMs and a local verifier to achieve high-accuracy, cost-effective code vulnerability detection, significantly outpe…

The paper introduces QuartetFuzz, an autonomous system that systematically ensures the correctness of fuzzing harnesses using a novel Four Principles framework, significantly improving vulnerability d…

GONDAR is a novel sink-centric fuzzing framework that systematically leverages vulnerability-specific knowledge to discover Java security flaws, significantly outperforming state-of-the-art fuzzers.