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 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…

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,…

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 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.

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…

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

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…

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

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…

This paper introduces a novel class of backdoor attacks that exploit the numerical side effects of LLM inference optimization, achieving high success rates while maintaining clean accuracy.

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…

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.

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…

This study formally verified 3,500 AI-generated code artifacts and found that a majority (55.8%) contain exploitable security vulnerabilities, regardless of the LLM used.

This paper quantifies the polymorphic capacity of a commercial LLM, demonstrating that it can cheaply generate large populations of structurally diverse, yet behaviorally equivalent, offensive code pa…