DiffCodeGen clusters code candidates by behavioral similarity from fuzzing-synthesized inputs and selects the largest cluster's medoid, matching or exceeding prior test-time scaling methods with far less token and time cost.
LLAMAFUZZ: Large Language Model Enhanced Greybox Fuzzing
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
ContentFuzz rewrites posts with LLM guidance from stance model confidence to flip machine labels without altering human intent, tested across four models and three datasets in two languages.
FunFuzz uses parallel LLM islands with candidate migration and adaptive prompting to achieve higher compiler coverage and more unique internal failures than prior LLM fuzzers on GCC and Clang over 24-hour runs.
SDLLMFuzz combines LLM-based generation of syntactically valid inputs with a dynamic-static feedback loop from crash artifacts to improve bug discovery and time-to-bug on structured-input programs compared to traditional and LLM-assisted fuzzers.
citing papers explorer
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Code Generation by Differential Test Time Scaling
DiffCodeGen clusters code candidates by behavioral similarity from fuzzing-synthesized inputs and selects the largest cluster's medoid, matching or exceeding prior test-time scaling methods with far less token and time cost.
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Content Fuzzing for Escaping Information Cocoons on Digital Social Media
ContentFuzz rewrites posts with LLM guidance from stance model confidence to flip machine labels without altering human intent, tested across four models and three datasets in two languages.
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FunFuzz: An LLM-Powered Evolutionary Fuzzing Framework
FunFuzz uses parallel LLM islands with candidate migration and adaptive prompting to achieve higher compiler coverage and more unique internal failures than prior LLM fuzzers on GCC and Clang over 24-hour runs.
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SDLLMFuzz: Dynamic-static LLM-assisted greybox fuzzing for structured input programs
SDLLMFuzz combines LLM-based generation of syntactically valid inputs with a dynamic-static feedback loop from crash artifacts to improve bug discovery and time-to-bug on structured-input programs compared to traditional and LLM-assisted fuzzers.