2,077+ academic papers on AI security, attacks, and defenses
Showing 521–540 of 986 papers
Clear filtersXuanjun Zong, Zhiqi Shen, Lei Wang +2 more
Large language models (LLMs) are evolving into agentic systems that reason, plan, and operate external tools. The Model Context Protocol (MCP) is a...
Richard Helder Moulton, Austin O'Brien, John D. Hastings
Although large language models (LLMs) are increasingly used in security-critical workflows, practitioners lack quantitative guidance on which...
Xinyu Huang, Shyam Karthick V B, Taozhao Chen +5 more
The integration of Large Language Models (LLMs) into robotics has revolutionized their ability to interpret complex human commands and execute...
Nnamdi Philip Okonkwo, Lubna Luxmi Dhirani
Cloud Security Operations Center (SOC) enable cloud governance, risk and compliance by providing insights visibility and control. Cloud SOC triages...
Viet K. Nguyen, Mohammad I. Husain
Agentic AI introduces security vulnerabilities that traditional LLM safeguards fail to address. Although recent work by Unit 42 at Palo Alto Networks...
Arth Bhardwaj, Sia Godika, Yuvam Loonker
Traditional, centralized security tools often miss adaptive, multi-vector attacks. We present the Multi-Agent LLM Cyber Defense Framework (MALCDF), a...
Yihan 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...
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...
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...
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...
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...
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...
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