FuzzAgent deploys specialized agents that collaborate on harness generation, execution, and crash triage to evolve fuzzing campaigns, delivering 45-191% more branch coverage than four baselines on 20 C/C++ libraries and surfacing 102 real bugs.
Promefuzz: A knowledge-driven approach to fuzzing harness generation with large language models
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SAILOR combines static analysis and LLM-orchestrated synthesis to automatically generate symbolic execution harnesses, discovering 379 previously unknown memory-safety vulnerabilities across 10 large open-source C/C++ projects where the strongest baseline found only 12.
MASFuzzer generates fuzz drivers via mined multidimensional API sequences and adaptive scheduling, delivering 8.54% higher code coverage and 16 new vulnerabilities across 12 libraries.
citing papers explorer
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FuzzAgent: Multi-Agent System for Evolutionary Library Fuzzing
FuzzAgent deploys specialized agents that collaborate on harness generation, execution, and crash triage to evolve fuzzing campaigns, delivering 45-191% more branch coverage than four baselines on 20 C/C++ libraries and surfacing 102 real bugs.
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Guiding Symbolic Execution with Static Analysis and LLMs for Vulnerability Discovery
SAILOR combines static analysis and LLM-orchestrated synthesis to automatically generate symbolic execution harnesses, discovering 379 previously unknown memory-safety vulnerabilities across 10 large open-source C/C++ projects where the strongest baseline found only 12.
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MASFuzzer: Fuzz Driver Generation and Adaptive Scheduling via Multidimensional API Sequences
MASFuzzer generates fuzz drivers via mined multidimensional API sequences and adaptive scheduling, delivering 8.54% higher code coverage and 16 new vulnerabilities across 12 libraries.