2,077+ academic papers on AI security, attacks, and defenses
Showing 181–200 of 355 papers
Clear filtersYihan Liao, Jacky Keung, Xiaoxue Ma +2 more
The rapid advancement of Large Language Models (LLMs) has been driven by extensive datasets that may contain sensitive information, raising serious...
Ruozhao Yang, Mingfei Cheng, Gelei Deng +3 more
Penetration testing is essential for assessing and strengthening system security against real-world threats, yet traditional workflows remain highly...
Akhil Sharma, Shaikh Yaser Arafat, Jai Kumar Sharma +1 more
The increasing operational reliance on complex Multi-Agent Systems (MAS) across safety-critical domains necessitates rigorous adversarial robustness...
Ali Al Sahili, Ali Chehab, Razane Tajeddine
Large Language Models (LLMs) are prone to memorizing training data, which poses serious privacy risks. Two of the most prominent concerns are...
Md Nahid Hasan Shuvo, Moinul Hossain
Connected autonomous vehicles (CAVs) rely on vision-based deep neural networks (DNNs) and low-latency (Vehicle-to-Everything) V2X communication to...
Sanjay Das, Swastik Bhattacharya, Shamik Kundu +3 more
State-space models (SSMs), exemplified by the Mamba architecture, have recently emerged as state-of-the-art sequence-modeling frameworks, offering...
Luoxi Meng, Henry Feng, Ilia Shumailov +1 more
Browser-using agents (BUAs) are an emerging class of AI agents that interact with web browsers in human-like ways, including clicking, scrolling,...
Arastoo Zibaeirad, Marco Vieira
Large Language Models (LLMs) are increasingly being studied for Software Vulnerability Detection (SVD) and Repair (SVR). Individual LLMs have...
Xin Yang, Omid Ardakanian
Data obfuscation is a promising technique for mitigating attribute inference attacks by semi-trusted parties with access to time-series data emitted...
Edward Lue Chee Lip, Anthony Channg, Diana Kim +2 more
As AI capabilities advance, we increasingly rely on powerful models to decompose complex tasks $\unicode{x2013}$ but what if the decomposer itself is...
Han Yang, Shaofeng Li, Tian Dong +3 more
Deep Neural Networks (DNNs), as valuable intellectual property, face unauthorized use. Existing protections, such as digital watermarking, are...
N Mangala, Murtaza Rangwala, S Aishwarya +5 more
Healthcare has become exceptionally sophisticated, as wearables and connected medical devices are revolutionising remote patient monitoring,...
Jan Betley, Jorio Cocola, Dylan Feng +4 more
LLMs are useful because they generalize so well. But can you have too much of a good thing? We show that a small amount of finetuning in narrow...
Aink Acrie Soe Thein, Nikolaos Pitropakis, Pavlos Papadopoulos +2 more
With the adoption of multiple digital devices in everyday life, the cyber-attack surface has increased. Adversaries are continuously exploring new...
Xinye Cao, Yihan Lin, Guoshun Nan +9 more
Zero-Touch Networks (ZTNs) represent a transformative paradigm toward fully automated and intelligent network management, providing the scalability...
Gary Ackerman, Zachary Kallenborn, Anna Wetzel +7 more
The potential for rapidly-evolving frontier artificial intelligence (AI) models, especially large language models (LLMs), to facilitate bioterrorism...
Md Nazmul Haque, Elizabeth Lin, Lawrence Arkoh +2 more
Large Language Models for code (LLMs4Code) are increasingly used to generate software artifacts, including library and package recommendations in...
Lukas Johannes Möller
The escalating sophistication and variety of cyber threats have rendered static honeypots inadequate, necessitating adaptive, intelligence-driven...
Jordan Taylor, Sid Black, Dillon Bowen +10 more
Future AI systems could conceal their capabilities ('sandbagging') during evaluations, potentially misleading developers and auditors. We...
JV Roig
We investigate how large language models (LLMs) fail when operating as autonomous agents with tool-use capabilities. Using the Kamiwaza Agentic Merit...
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