2,529+ academic papers on AI security, attacks, and defenses
Showing 1–20 of 27 papers
Clear filtersNikita Kezins, Urbas Ekka, Pascal Berrang +1 more
Guardrail Classifiers defend production language models against harmful behavior, but although results seem promising in testing, they provide no...
Wenxin Tang, Xiang Zhang, Junliang Liu +11 more
Automated vulnerability detection is a fundamental task in software security, yet existing learning-based methods still struggle to capture the...
Zeming Dong, Yuejun Guo, Qiang Hu +5 more
Source code and its accompanying comments are complementary yet naturally aligned modalities-code encodes structural logic while comments capture...
Mohamed Taoufik Kaouthar El Idrissi, Edward Zulkoski, Mohammad Hamdaqa
Code understanding models increasingly rely on pretrained language models (PLMs) and graph neural networks (GNNs), which capture complementary...
Zhaohui Geoffrey Wang
Automated code vulnerability detection is critical for software security, yet existing approaches face a fundamental trade-off between detection...
Ronghao Ni, Mihai Christodorescu, Limin Jia
The rapidly evolving Node$.$js ecosystem currently includes millions of packages and is a critical part of modern software supply chains, making...
Kevin Lira, Baldoino Fonseca, Davy Baía +2 more
Large Language Models (LLMs) have been a promising way for automated vulnerability detection. However, most prior studies have explored the use of...
Zi Liang, Qipeng Xie, Jun He +7 more
Recent advancements in Large Language Models (LLMs) have sparked interest in their application to Static Application Security Testing (SAST),...
Ayush Garg, Sophia Hager, Jacob Montiel +5 more
Security teams face a challenge: the volume of newly disclosed Common Vulnerabilities and Exposures (CVEs) far exceeds the capacity to manually...
Miles Farmer, Ekincan Ufuktepe, Anne Watson +4 more
Large Language Models (LLMs) have emerged as a popular choice in vulnerability detection studies given their foundational capabilities, open source...
Aymen Lassoued, Nacef Mbarek, Bechir Dardouri +3 more
Vulnerability detection in C programs is a critical challenge in software security. Although large language models (LLMs) achieve strong detection...
Samal Mukhtar, Yinghua Yao, Zhu Sun +3 more
Software vulnerability detection (SVD) is a critical challenge in modern systems. Large language models (LLMs) offer natural-language explanations...
Zihan Wu, Jie Xu, Yun Peng +2 more
Large Language Models (LLMs) struggle to automate real-world vulnerability detection due to two key limitations: the heterogeneity of vulnerability...
Jonah Ghebremichael, Saastha Vasan, Saad Ullah +6 more
Static Application Security Testing (SAST) tools using taint analysis are widely viewed as providing higher-quality vulnerability detection results...
Hao Wang, Yanting Wang, Hao Li +2 more
Large Language Models (LLMs) have achieved remarkable capabilities but remain vulnerable to adversarial ``jailbreak'' attacks designed to bypass...
Yun Bian, Yi Chen, HaiQuan Wang +2 more
Software vulnerability detection can be formulated as a binary classification problem that determines whether a given code snippet contains security...
Toqeer Ali Syed, Mohammad Riyaz Belgaum, Salman Jan +2 more
The software supply chain attacks are becoming more and more focused on trusted development and delivery procedures, so the conventional post-build...
Dyna Soumhane Ouchebara, Stéphane Dupont
The significant increase in software production, driven by the acceleration of development cycles over the past two decades, has led to a steady rise...
Fouad Trad, Ali Chehab
Few-shot prompting has emerged as a practical alternative to fine-tuning for leveraging the capabilities of large language models (LLMs) in...
Jie Chen, Liangmin Wang
Fuzzing is a widely used technique for detecting vulnerabilities in smart contracts, which generates transaction sequences to explore the execution...
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