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Recent research advances in Artificial Intelligence (AI) have yielded promising results for automated software vulnerability management.
Automatic patch-based exploit generation is possible: Techniques and implications
David Brumley, Pongsin Poosankam, Dawn Song, and Jiang Zheng · 2008
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Automatic generation of xss and sql injection attacks with goal-directed model checking
Michael C Martin and Monica S Lam · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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The adverse effects of code duplication in machine learning models of code
Miltiadis Allamanis · 2019
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code2vec: Learning distributed representations of code
Uri Alon, Meital Zilberstein, Omer Levy, and Eran Yahav · 2019
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Gollum: Modular and greybox exploit generation for heap overflows in interpreters
Sean Heelan, Tom Melham, and Daniel Kroening · 2019
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Detecting” 0-day” vulnerability: An empirical study of secret security patch in oss
Xinda Wang, Kun Sun, Archer Batcheller, and Sushil Jajodia · 2019
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Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks
Yaqin Zhou, Shangqing Liu, Jingkai Siow, Xiaoning Du, and Yang Liu · 2019
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Software vulnerability detection using deep neural networks: a survey
Guanjun Lin, Sheng Wen, Qing-Long Han, Jun Zhang, and Yang Xiang · 2020
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Deep learning based vulnerability detection: Are we there yet
Saikat Chakraborty, Rahul Krishna, Yangruibo Ding, and Baishakhi Ray · 2021
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The rise of software vulnerability: Taxonomy of software vulnerabilities detection and machine learning approaches
Hazim Hanif, Mohd Hairul Nizam Md Nasir, Mohd Faizal Ab Razak, Ahmad Firdaus, and Nor Badrul Anuar · 2021
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Project CodeNet
IBM · 2021
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Vulnerability detection with fine-grained interpretations
Yi Li, Shaohua Wang, and Tien N Nguyen · 2021
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Shallow or deep? an empirical study on detecting vulnerabilities using deep learning
Alejandro Mazuera-Rozo, Anamaria Mojica-Hanke, Mario Linares-Vásquez, and Gabriele Bavota · 2021
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CWE Top 25 Most Dangerous Software Weaknesses
MITRE · 2021
Cited alongside, same era.
Patchdb: A large-scale security patch dataset
Xinda Wang, Shu Wang, Pengbin Feng, Kun Sun, and Sushil Jajodia · 2021
Cited alongside, same era.
Patchrnn: A deep learning-based system for security patch identification
Xinda Wang, Shu Wang, Pengbin Feng, Kun Sun, Sushil Jajodia, Sanae Benchaaboun, and Frank Geck · 2021
Cited alongside, same era.
Prevention of phishing attacks using ai-based cybersecurity awareness training
Meraj Farheen Ansari, Pawan Kumar Sharma, and Bibhu Dash · 2022
Cited alongside, same era.
Fira: fine-grained graph-based code change representation for automated commit message generation
Jinhao Dong, Yiling Lou, Qihao Zhu, Zeyu Sun, Zhilin Li, Wenjie Zhang, and Dan Hao · 2022
Cited alongside, same era.
Linevul: A transformer-based line-level vulnerability prediction
Diversevul: A new vulnerable source code dataset for deep learning based vulnerability detection
Yizheng Chen, Zhoujie Ding, Lamya Alowain, Xinyun Chen, and David Wagner · 2023
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Data quality for software vulnerability datasets
Roland Croft, M Ali Babar, and M Mehdi Kholoosi · 2023
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Darpa ai cyber challenge aims to secure nation’s most critical software, 2023
DARPA · 2023
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https://githubuniverse.com , 2023
GitHub Universe · 2023
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Swe-bench: Can language models resolve real-world github issues?
Carlos E Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan · 2023
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What is vulnerability management?
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Michael Fu and Chakkrit Tantithamthavorn · 2022
Cited alongside, same era.
Competitive programming with alphacode
Google DeepMind · 2022
Cited alongside, same era.
On distribution shift in learning-based bug detectors
Jingxuan He, Luca Beurer-Kellner, and Martin Vechev · 2022
Cited alongside, same era.
Open science in software engineering: A study on deep learning-based vulnerability detection
Yu Nong, Rainy Sharma, Abdelwahab Hamou-Lhadj, Xiapu Luo, and Haipeng Cai · 2022
Cited alongside, same era.
Autotransform: Automated code transformation to support modern code review process
Patanamon Thongtanunam, Chanathip Pornprasit, and Chakkrit Tantithamthavorn · 2022
Cited alongside, same era.
Using pre-trained models to boost code review automation
Rosalia Tufano, Simone Masiero, Antonio Mastropaolo, Luca Pascarella, Denys Poshyvanyk, and Gabriele Bavota · 2022
Cited alongside, same era.
SQIRL:Grey-Box Detection of SQL Injection Vulnerabilities Using Reinforcement Learning
Salim Al Wahaibi, Myles Foley, and Sergio Maffeis · 2023
Cited alongside, same era.
Microsoft Security · 2023
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Vulchecker: Graph-based vulnerability localization in source code
Yisroel Mirsky, George Macon, Michael Brown, Carter Yagemann, Matthew Pruett, Evan Downing, Sukarno Mertoguno, and Wenke Lee · 2023
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Examining zero-shot vulnerability repair with large language models
Hammond Pearce, Benjamin Tan, Baleegh Ahmad, Ramesh Karri, and Brendan Dolan-Gavitt · 2023
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Code llama: Open foundation models for code
Baptiste Roziere, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, et al · 2023
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An empirical study of deep learning models for vulnerability detection
Benjamin Steenhoek, Md Mahbubur Rahman, Richard Jiles, and Wei Le · 2023
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GraphSPD: Graph-based security patch detection with enriched code semantics
Shu Wang, Xinda Wang, Kun Sun, Sushil Jajodia, Haining Wang, and Qi Li · 2023
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Automated program repair in the era of large pre-trained language models
Chunqiu Steven Xia, Yuxiang Wei, and Lingming Zhang · 2023
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Enhancing deep learning-based vulnerability detection by building behavior graph model
Bin Yuan, Yifan Lu, Yilin Fang, Yueming Wu, Deqing Zou, Zhen Li, Zhi Li, and Hai Jin · 2023
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