2,077+ academic papers on AI security, attacks, and defenses
Showing 41–60 of 63 papers
Clear filtersAlexander K. Saeri, Sophia Lloyd George, Jess Graham +4 more
Organizations and governments that develop, deploy, use, and govern AI must coordinate on effective risk mitigation. However, the landscape of AI...
Shiva Gaire, Srijan Gyawali, Saroj Mishra +3 more
The Model Context Protocol (MCP) has emerged as the de facto standard for connecting Large Language Models (LLMs) to external data and tools,...
Guanquan Shi, Haohua Du, Zhiqiang Wang +4 more
Large Language Models (LLMs) are evolving into autonomous agents capable of executing complex workflows via standardized protocols (e.g., MCP)....
Mehrab Hosain, Sabbir Alom Shuvo, Matthew Ogbe +4 more
The modern web stack, which is dominated by browser-based applications and API-first backends, now operates under an adversarial equilibrium where...
Wei Zhao, Zhe Li, Jun Sun
Large Language Models (LLMs) exhibit remarkable capabilities but remain vulnerable to adversarial manipulations such as jailbreaking, where crafted...
Jaehwan Park, Kyungchan Lim, Seonhye Park +1 more
The advent of Artificial Intelligence (AI), particularly large language models (LLMs), has revolutionized software development by enabling developers...
Jiangrui Zheng, Yingming Zhou, Ali Abdullah Ahmad +2 more
Bug bounty platforms (e.g., HackerOne, BugCrowd) leverage crowd-sourced vulnerability discovery to improve continuous coverage, reduce the cost of...
James Jin Kang, Dang Bui, Thanh Pham +1 more
The growing use of large language models in sensitive domains has exposed a critical weakness: the inability to ensure that private information can...
Gabrielle M Gauthier, Eesha Ali, Amna Asim +2 more
Human content moderators (CMs) routinely review distressing digital content at scale. Beyond exposure, the work context (e.g., workload, team...
Kathrin Grosse, Nico Ebert
Recent improvement gains in large language models (LLMs) have lead to everyday usage of AI-based Conversational Agents (CAs). At the same time, LLMs...
Robert A. Bridges, Thomas R. Mitchell, Mauricio Muñoz +1 more
The advent of Large Language Models (LLMs) promised to resolve the long-standing paradox in honeypot design, achieving high-fidelity deception with...
Bin Wang, Zexin Liu, Hao Yu +6 more
The Model Context Protocol (MCP) has emerged as a standardized interface enabling seamless integration between Large Language Models (LLMs) and...
Ronghao Ni, Aidan Z. H. Yang, Min-Chien Hsu +5 more
Program analysis tools often produce large volumes of candidate vulnerability reports that require costly manual review, creating a practical...
Oleksandr Adamov, Anders Carlsson
This paper explores the challenges of cyberattack attribution, specifically APTs, applying the case study approach for the WhisperGate cyber...
Hanbin Hong, Shuya Feng, Nima Naderloui +6 more
Large Language Models (LLMs) have rapidly become integral to real-world applications, powering services across diverse sectors. However, their...
Zhenyu Mao, Jacky Keung, Fengji Zhang +3 more
The increasing demand for software development has driven interest in automating software engineering (SE) tasks using Large Language Models (LLMs)....
Man Hu, Xinyi Wu, Zuofeng Suo +5 more
With the rise of advanced reasoning capabilities, large language models (LLMs) are receiving increasing attention. However, although reasoning...
Chongyu Fan, Changsheng Wang, Yancheng Huang +2 more
Machine unlearning for large language models (LLMs) aims to remove undesired data, knowledge, and behaviors (e.g., for safety, privacy, or copyright)...
Cade Houston Kennedy, Amr Hilal, Morteza Momeni
With the growth of digital financial systems, robust security and privacy have become a concern for financial institutions. Even though traditional...
Niloofar Mireshghallah, Tianshi Li
The discourse on privacy risks in Large Language Models (LLMs) has disproportionately focused on verbatim memorization of training data, while a...
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