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…

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…

FuzzPilot is a controller for AFL++ that validates candidate mutation recipes by running short micro-campaigns, demonstrating a mechanism to manage fuzzing plateaus, though initial results on a satura…

Weaver is a novel greybox fuzzing framework designed to uncover security vulnerabilities at the complex interaction boundary between JavaScript and WebAssembly, achieving superior code coverage and fi…

The paper introduces PeAR, a static binary rewriting framework that proves static binary instrumentation (SBI) is a practical and effective alternative to dynamic binary instrumentation (DBI) for high…

FuzzingBrain V2 is a multi-agent LLM system that significantly improves automated vulnerability discovery by ensuring all reported bugs are fuzzer-reproducible and handling complex cross-function depe…

The paper proposes a general, compiler-integrated framework for secure content composition that minimizes the syntactic difference between secure and insecure coding practices.

The paper proposes TriageFuzz, a token-aware fuzzing framework that significantly reduces the number of queries needed to jailbreak LLMs while maintaining high attack success rates.

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.

The paper introduces Sefz, a semantic fuzzing framework that automatically discovers specification violations in LLM agent skills, finding a significant number of previously unknown exploitable guardr…

The paper introduces CAT, a novel coverage-guided fuzzing tool that overcomes the limitations of existing fuzzers for complex, multi-object cryptographic repositories like RPKI, leading to the discove…

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…

SEMBridge is a tagless-final framework that allows a single executable object program to generate multiple program semantics, including weakest-precondition and bounded-checking interpretations, ensur…

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 memory forensics framework to perform runtime analysis of Go malware, successfully recovering critical execution state and artifacts that are invisible to traditional stat…

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 introduces codebadger, a Model Context Protocol (MCP) server that integrates Joern's Code Property Graph (CPG) with LLMs, enabling large language models to perform large-scale, semantic prog…

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…

TEMPLATEFUZZ is a fine-grained fuzzing framework that systematically tests chat templates to find vulnerabilities in LLMs, achieving high jailbreak success rates with minimal performance degradation.

FPMoE introduces a sparse Mixture-of-Experts (MoE) architecture to improve functional code generation across multiple functional programming languages, achieving state-of-the-art performance with fewe…