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.
How do programmers use unsafe rust?
4 Pith papers cite this work. Polarity classification is still indexing.
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Kani is a model checker that compiles Rust proof harnesses from MIR to CBMC for bounded verification of safety properties and supports contracts to extend checks to unbounded correctness.
A large open crowdsourced effort verifies substantial parts of the Rust standard library for memory safety properties by integrating complementary verification tools into CI on a forked repository.
Reinforcement learning on MIR features combined with cargo-fuzz validation reduces false positives in Rust static memory safety analysis, raising precision from 25.6% to 59.0% and accuracy to 65.2%.
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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Kani: A Model Checker for Rust
Kani is a model checker that compiles Rust proof harnesses from MIR to CBMC for bounded verification of safety properties and supports contracts to extend checks to unbounded correctness.
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Verifying the Rust Standard Library
A large open crowdsourced effort verifies substantial parts of the Rust standard library for memory safety properties by integrating complementary verification tools into CI on a forked repository.
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Mitigating False Positives in Static Memory Safety Analysis of Rust Programs via Reinforcement Learning
Reinforcement learning on MIR features combined with cargo-fuzz validation reduces false positives in Rust static memory safety analysis, raising precision from 25.6% to 59.0% and accuracy to 65.2%.