ClozeMaster masks bracketed structures in historical Rust bug code and uses LLMs to infill them, generating test programs that discovered 27 confirmed bugs in rustc and mrustc while outperforming existing fuzzers.
Large language models are edge-case fuzzers: Testing deep learning libraries via fuzzgpt
6 Pith papers cite this work, alongside 23 external citations. Polarity classification is still indexing.
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representative citing papers
Once4All synthesizes LLM-based generators from extracted SMT grammars and populates formula skeletons to fuzz Z3 and cvc5, discovering 43 confirmed bugs with 40 fixed.
Reddit analysis of 892 posts shows SOC practitioners adopt LLMs mainly for controllable productivity work and constrain autonomy due to reliability, security, and cost concerns.
ParityFuzz finds 64 new inconsistencies across six Solidity compilers by combining fine-grained mutation rules with reinforcement learning for differential testing.
TEMPLATEFUZZ mutates chat templates with element-level rules and heuristic search to reach 98.2% average jailbreak success rate on twelve open-source LLMs while degrading accuracy by only 1.1%.
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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ClozeMaster: Fuzzing Rust Compiler by Harnessing LLMs for Infilling Masked Real Programs
ClozeMaster masks bracketed structures in historical Rust bug code and uses LLMs to infill them, generating test programs that discovered 27 confirmed bugs in rustc and mrustc while outperforming existing fuzzers.
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Once4All: Skeleton-Guided SMT Solver Fuzzing with LLM-Synthesized Generators
Once4All synthesizes LLM-based generators from extracted SMT grammars and populates formula skeletons to fuzz Z3 and cvc5, discovering 43 confirmed bugs with 40 fixed.
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Like a Hammer, It Can Build, It Can Break: Large Language Model Uses, Perceptions, and Adoption in Cybersecurity Operations on Reddit
Reddit analysis of 892 posts shows SOC practitioners adopt LLMs mainly for controllable productivity work and constrain autonomy due to reliability, security, and cost concerns.
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ParityFuzz: Finding Inconsistencies across Solidity Compilers via Fine-Grained Mutation and Differential Analysis
ParityFuzz finds 64 new inconsistencies across six Solidity compilers by combining fine-grained mutation rules with reinforcement learning for differential testing.
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TEMPLATEFUZZ: Fine-Grained Chat Template Fuzzing for Jailbreaking and Red Teaming LLMs
TEMPLATEFUZZ mutates chat templates with element-level rules and heuristic search to reach 98.2% average jailbreak success rate on twelve open-source LLMs while degrading accuracy by only 1.1%.
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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.