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  • When Less is Enough: Positive and Unlabeled Learning Model for . . .
    In this paper, we focus on the Positive and Unlabeled (PU) learning problem for vulnerability detection and propose a novel model named PILOT, i e , PositIve and unlabeled Learning mOdel for vulnerability deTection PILOT only learns from positive and unlabeled data for vulnerability detection
  • When Dataflow Analysis Meets Large Language Models
    Dataflow analysis is a powerful code analysis tech-nique that reasons dependencies between program values, offering support for code optimization, program comprehension, and bug detection Ex-isting approaches require the successful compila-tion of the subject program and customizations for downstream applications
  • LinuxのLinux Kernelにおけるゼロ除算に関する脆弱性
    ウェブアプリケーション診断、セキュリティに関する情報を提供しているサイトです。すぐに脆弱性診断を実施したい、予算が少ないが診断が必要な場合はSecuAliveが解決できます。
  • LLMDFA: Analyzing Dataflow in Code with Large Language Models
    Dataflow analysis is a fundamental code analysis technique that identifies depen-dencies between program values Traditional approaches typically necessitate successful compilation and expert customization, hindering their applicability and usability for analyzing uncompilable programs with evolving analysis needs in real-world scenarios
  • LLMDFA | Proceedings of the 38th International Conference on Neural . . .
    We evaluate LLMDFA on synthetic programs to detect three representative types of bugs and on real-world Android applications for customized bug detection On average, LLMDFA achieves 87 10% precision and 80 77% recall, surpassing existing techniques with F1 score improvements of up to 0 35





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