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…

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 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 FIDO, a novel framework that significantly boosts firmware fuzzing efficiency by accurately managing the timing and quantity of input delivery based on the firmware's internal inp…

The paper proposes MTCFuzz, a multi-target coverage-based greybox fuzzer, to deeply explore vulnerabilities in modern system architectures where an operating system and firmware cooperate.

OverrideFuzz is a novel semantic-aware grammar fuzzer designed to test script-language runtimes by specifically modeling and exploiting complex behaviors like method overriding and dynamic rebinding,…

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…

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

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…

PyFEX introduces a resilient forced-execution engine to exhaustively analyze Python code, successfully detecting previously unknown malicious packages and binaries in the Python ecosystem.

The paper introduces Heimdall, an automated pipeline that uses LLMs and formal verification to safely and automatically migrate legacy, potentially buggy eBPF programs written in C to memory-safe Rust…

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…

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…

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 introduces BOUNDARY FLOW, an LLVM-based framework that enhances kernel fuzzing and analysis by extracting per-task, state-aware data-flow information (arguments and return values) at functio…

The paper introduces EXHIB, a comprehensive benchmark of five real-world datasets, to evaluate Function Similarity Detection, demonstrating that current models fail to generalize across diverse low- a…

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…

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…

The paper introduces PickleFuzzer, a custom fuzzer that identifies security-critical discrepancies across different Python pickle implementations, finding 14 new bugs including four that could bypass…