Empirical evaluation of three LLMs finds prevalent overconfidence in insecure code generation, with security calibration outperforming functional calibration but both degrading in repository-level settings.
Fuzz4all: Universal fuzzing with large language models
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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.
A Lean library called Palamedes uses synthesis rules from generator semantics and catamorphism-anamorphism rewriting to automatically produce correct constrained random 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.
AdaTrans uses strategy-driven RAG, error-stratified transformation, and multi-stage validation to reach 95.51% mean compilation pass rate and 81.09% solve rate on 104 algorithmic problems with only 1.19% unsafe files.
MR-Adopt deduces input transformations from hard-coded MR test cases using LLMs, data-flow refinement, and output-relation selection to enable reuse with new source inputs.
citing papers explorer
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An Empirical Study of Security Calibration in Large Language Models for Code
Empirical evaluation of three LLMs finds prevalent overconfidence in insecure code generation, with security calibration outperforming functional calibration but both degrading in repository-level settings.
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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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The Search for Constrained Random Generators
A Lean library called Palamedes uses synthesis rules from generator semantics and catamorphism-anamorphism rewriting to automatically produce correct constrained random generators.
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AdaTrans: Automated C to Rust Transformation via Error-Adaptive Repair
AdaTrans uses strategy-driven RAG, error-stratified transformation, and multi-stage validation to reach 95.51% mean compilation pass rate and 81.09% solve rate on 104 algorithmic problems with only 1.19% unsafe files.
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MR-Adopt: Automatic Deduction of Input Transformation Function for Metamorphic Testing
MR-Adopt deduces input transformations from hard-coded MR test cases using LLMs, data-flow refinement, and output-relation selection to enable reuse with new source inputs.