2,077+ academic papers on AI security, attacks, and defenses
Showing 701–720 of 2,023 papers
Clear filtersMingqiao Mo, Yunlong Tan, Hao Zhang +2 more
Large language models (LLMs) have achieved remarkable progress in code generation, yet their potential for software protection remains largely...
Xingwei Lin, Wenhao Lin, Sicong Cao +4 more
Multi-turn jailbreak attacks have emerged as a critical threat to Large Language Models (LLMs), bypassing safety mechanisms by progressively...
Yizhong Ding
Webshells remain a primary foothold for attackers to compromise servers, particularly within PHP ecosystems. However, existing detection mechanisms...
Holly Trikilis, Pasindu Marasinghe, Fariza Rashid +1 more
Phishing continues to be one of the most prevalent attack vectors, making accurate classification of phishing URLs essential. Recently, large...
Mohsen Hatami, Van Tuan Pham, Hozefa Lakadawala +1 more
The increasing integration of AI agents into cyber-physical systems (CPS) introduces new security risks that extend beyond traditional cyber or...
Yuetian Chen, Kaiyuan Zhang, Yuntao Du +5 more
Diffusion Language Models (DLMs) represent a promising alternative to autoregressive language models, using bidirectional masked token prediction....
Faezeh Hosseini, Mohammadali Yousefzadeh, Yadollah Yaghoobzadeh
Figurative language, particularly fixed figurative expressions (FFEs) such as idioms and proverbs, poses persistent challenges for large language...
Md Tasnim Jawad, Mingyan Xiao, Yanzhao Wu
With the widespread adoption of Large Language Models (LLMs) and increasingly stringent privacy regulations, protecting data privacy in LLMs has...
Pragatheeswaran Vipulanandan, Kamal Premaratne, Dilip Sarkar
Large language models (LLMs) exhibit strong generative capabilities but remain vulnerable to confabulations, fluent yet unreliable outputs that vary...
Bharath Krishnamurthy, Ajita Rattani
Morphing techniques generate artificial biometric samples that combine features from multiple individuals, allowing each contributor to be verified...
Nourin Shahin, Izzat Alsmadi
As large language models (LLMs) move from research prototypes to enterprise systems, their security vulnerabilities pose serious risks to data...
Lige Huang, Zicheng Liu, Jie Zhang +3 more
The dual offensive and defensive utility of Large Language Models (LLMs) highlights a critical gap in AI security: the lack of unified frameworks for...
Xiangyang Zhu, Yuan Tian, Zicheng Zhang +6 more
Large vision-language models (LVLMs) exhibit remarkable capabilities in cross-modal tasks but face significant safety challenges, which undermine...
Haonan Zhang, Dongxia Wang, Yi Liu +2 more
Safety-aligned LLMs suffer from two failure modes: jailbreak (answering harmful inputs) and over-refusal (declining benign queries). Existing vector...
Binyan Xu, Fan Yang, Xilin Dai +2 more
Deep Neural Networks remain inherently vulnerable to backdoor attacks. Traditional test-time defenses largely operate under the paradigm of internal...
Eranga Bandara, Ross Gore, Sachin Shetty +9 more
6G networks are expected to be AI-native, intent-driven, and economically programmable, requiring fundamentally new approaches to network slice...
Xingcheng Xu, Jingjing Qu, Qiaosheng Zhang +4 more
The rapid deployment of Large Language Models and AI agents across critical societal and technical domains is hindered by persistent behavioral...
Quy-Anh Dang, Chris Ngo
Despite significant progress in alignment, large language models (LLMs) remain vulnerable to adversarial attacks that elicit harmful behaviors....
Yuxiang Wang, Hongyu Liu, Dekun Chen +2 more
As Speech Language Models (SLMs) transition from personal devices to shared, multi-user environments such as smart homes, a new challenge emerges:...
Yangyang Guo, Ziwei Xu, Si Liu +2 more
This study reveals a previously unexplored vulnerability in the safety alignment of Large Language Models (LLMs). Existing aligned LLMs predominantly...
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