2,589+ academic papers on AI security, attacks, and defenses
Showing 1641–1660 of 2,589 papers
Teodor Poncu, Ioana Pintilie, Marius Dragoi +2 more
Large Language Models (LLMs) typically excel at coding tasks involving high-level programming languages, as opposed to lower-level programming...
Piercosma Bisconti, Marcello Galisai, Matteo Prandi +6 more
Safety mechanisms in LLMs remain vulnerable to attacks that reframe harmful requests through culturally coded structures. We introduce Adversarial...
Xingfu Zhou, Pengfei Wang
Large Language Model (LLM) agents relying on external retrieval are increasingly deployed in high-stakes environments. While existing adversarial...
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...
Yunhao Yao, Zhiqiang Wang, Haoran Cheng +3 more
The evolution of Large Language Models (LLMs) into Agentic AI has established the Model Context Protocol (MCP) as the standard for connecting...
Shuxin Zhao, Bo Lang, Nan Xiao +1 more
Object detection models deployed in real-world applications such as autonomous driving face serious threats from backdoor attacks. Despite their...
Dongchao Zhou, Lingyun Ying, Huajun Chai +1 more
JavaScript's widespread adoption has made it an attractive target for malicious attackers who employ sophisticated obfuscation techniques to conceal...
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...
Asa Cooper Stickland, Jan Michelfeit, Arathi Mani +6 more
LLM-based software engineering agents are increasingly used in real-world development tasks, often with access to sensitive data or security-critical...
Sabrine Ennaji, Elhadj Benkhelifa, Luigi Vincenzo Mancini
Machine learning based intrusion detection systems are increasingly targeted by black box adversarial attacks, where attackers craft evasive inputs...
David Lindner, Charlie Griffin, Tomek Korbak +4 more
Automated control monitors could play an important role in overseeing highly capable AI agents that we do not fully trust. Prior work has explored...
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...
Karina Chichifoi, Fabio Merizzi, Michele Colajanni
Deep learning and federated learning (FL) are becoming powerful partners for next-generation weather forecasting. Deep learning enables...
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...
Amy Chang, Tiffany Saade, Sanket Mendapara +2 more
Artificial intelligence (AI) systems are being readily and rapidly adopted, increasingly permeating critical domains: from consumer platforms and...
Shashie Dilhara Batan Arachchige, Benjamin Zi Hao Zhao, Hassan Jameel Asghar +2 more
Large Language Models (LLMs) are often fine-tuned to adapt their general-purpose knowledge to specific tasks and domains such as cyber threat...
Perry Abdulkadir
Large language models (LLMs) are increasingly deployed behind safety guardrails such as system prompts and content filters, especially in settings...
Samruddhi Baviskar
We evaluate adversarial robustness in tabular machine learning models used in financial decision making. Using credit scoring and fraud detection...
Mohammad Mahdi Razmjoo, Mohammad Mahdi Sharifian, Saeed Bagheri Shouraki
Despite their remarkable performance, deep neural networks exhibit a critical vulnerability: small, often imperceptible, adversarial perturbations...
Li Lin, Siyuan Xin, Yang Cao +1 more
Watermarking large language models (LLMs) is vital for preventing their misuse, including the fabrication of fake news, plagiarism, and spam. It is...
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