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Uncovering the Limits of Machine Learning for Automatic Vulnerability Detection

Uncovering the Limits of Machine Learning for Automatic Vulnerability Detection

Niklas Risse
Marcel Bohme
    AAML
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Papers citing "Uncovering the Limits of Machine Learning for Automatic Vulnerability Detection"

5 / 5 papers shown
Title
Poster: Machine Learning for Vulnerability Detection as Target Oracle in Automated Fuzz Driver Generation
Poster: Machine Learning for Vulnerability Detection as Target Oracle in Automated Fuzz Driver Generation
Gianpietro Castiglione
Marcello Maugeri
G. Bella
12
0
0
02 May 2025
Top Score on the Wrong Exam: On Benchmarking in Machine Learning for Vulnerability Detection
Top Score on the Wrong Exam: On Benchmarking in Machine Learning for Vulnerability Detection
Niklas Risse
Marcel Bohme
Marcel Böhme
38
4
0
23 Aug 2024
VulBERTa: Simplified Source Code Pre-Training for Vulnerability Detection
VulBERTa: Simplified Source Code Pre-Training for Vulnerability Detection
Hazim Hanif
S. Maffeis
58
95
0
25 May 2022
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
Shuai Lu
Daya Guo
Shuo Ren
Junjie Huang
Alexey Svyatkovskiy
...
Nan Duan
Neel Sundaresan
Shao Kun Deng
Shengyu Fu
Shujie Liu
ELM
196
1,103
0
09 Feb 2021
Semantic Robustness of Models of Source Code
Semantic Robustness of Models of Source Code
Goutham Ramakrishnan
Jordan Henkel
Zi Wang
Aws Albarghouthi
S. Jha
Thomas W. Reps
SILM
AAML
35
97
0
07 Feb 2020