2,583+ academic papers on AI security, attacks, and defenses
Showing 461–480 of 656 papers
Clear filtersOleg Brodt, Elad Feldman, Bruce Schneier +1 more
Prompt injection was initially framed as the large language model (LLM) analogue of SQL injection. However, over the past three years, attacks...
Zhiyi Mou, Jingyuan Yang, Zeheng Qian +6 more
While Large Language Models (LLMs) have powerful capabilities, they remain vulnerable to jailbreak attacks, which is a critical barrier to their safe...
Xiaonan Liu, Zhihao Li, Xiao Lan +3 more
Capture-the-Flag (CTF) competitions play a central role in modern cybersecurity as a platform for training practitioners and evaluating offensive and...
Fengchao Chen, Tingmin Wu, Van Nguyen +1 more
Large Language Models (LLMs) have enabled agents to move beyond conversation toward end-to-end task execution and become more helpful. However, this...
Shaznin Sultana, Sadia Afreen, Nasir U. Eisty
Context: Traditional software security analysis methods struggle to keep pace with the scale and complexity of modern codebases, requiring...
Mohammed Himayath Ali, Mohammed Aqib Abdullah, Mohammed Mudassir Uddin +1 more
Large Language Models have emerged as transformative tools for Security Operations Centers, enabling automated log analysis, phishing triage, and...
Xinyi Wu, Geng Hong, Yueyue Chen +5 more
Web agents, powered by large language models (LLMs), are increasingly deployed to automate complex web interactions. The rise of open-source...
Shawn Li, Chenxiao Yu, Zhiyu Ni +4 more
Large language models (LLMs) are increasingly deployed in security-sensitive applications, where they must follow system- or developer-specified...
Hongyan Chang, Ergute Bao, Xinjian Luo +1 more
Large language models (LLMs) increasingly rely on retrieving information from external corpora. This creates a new attack surface: indirect prompt...
Harshil Parmar, Pushti Vyas, Prayers Khristi +1 more
As vulnerability research increasingly adopts generative AI, a critical reliance on opaque model outputs has emerged, creating a "trust gap" in...
Masahiro Kaneko
The use of large language models (LLMs) in peer review systems has attracted growing attention, making it essential to examine their potential...
Muhammad Wahid Akram, Keshav Sood, Muneeb Ul Hassan +1 more
Phishing with Quick Response (QR) codes is termed as Quishing. The attackers exploit this method to manipulate individuals into revealing their...
Quan Minh Nguyen, Min-Seon Kim, Hoang M. Ngo +3 more
Membership inference attack (MIA) poses a significant privacy threat in federated learning (FL) as it allows adversaries to determine whether a...
Hongjun An, Yiliang Song, Jiangan Chen +3 more
Large Language Model (LLM) training often optimizes for preference alignment, rewarding outputs that are perceived as helpful and...
Víctor Mayoral-Vilches, María Sanz-Gómez, Francesco Balassone +6 more
AI-driven penetration testing now executes thousands of actions per hour but still lacks the strategic intuition humans apply in competitive...
Junda Lin, Zhaomeng Zhou, Zhi Zheng +4 more
LLM agents operating in open environments face escalating risks from indirect prompt injection, particularly within the tool stream where manipulated...
Ahmad Alobaid, Martí Jordà Roca, Carlos Castillo +1 more
The availability of Large Language Models (LLMs) has led to a new generation of powerful chatbots that can be developed at relatively low cost. As...
Jingxiao Yang, Ping He, Tianyu Du +2 more
Recent advances in software vulnerability detection have been driven by Language Model (LM)-based approaches. However, these models remain vulnerable...
Balachandra Devarangadi Sunil, Isheeta Sinha, Piyush Maheshwari +3 more
Large language model agents equipped with persistent memory are vulnerable to memory poisoning attacks, where adversaries inject malicious...
Zhaoqi Wang, Zijian Zhang, Daqing He +5 more
Large language models (LLMs) have demonstrated remarkable capabilities across diverse applications, however, they remain critically vulnerable to...
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