2,077+ academic papers on AI security, attacks, and defenses
Showing 601–620 of 965 papers
Clear filtersThomas Rivasseau
Current research on operator control of Large Language Models improves model robustness against adversarial attacks and misbehavior by training on...
Yepeng Ding, Ahmed Twabi, Junwei Yu +3 more
The emergence of Large Language Models (LLMs) is rapidly accelerating the development of autonomous multi-agent systems (MAS), paving the way for the...
Weiwei Wang
Catastrophic forgetting remains a fundamental challenge in continual learning for large language models. Recent work revealed that performance...
Junyu Wang, Changjia Zhu, Yuanbo Zhou +3 more
This paper studies how multimodal large language models (MLLMs) undermine the security guarantees of visual CAPTCHA. We identify the attack surface...
Adel Chehade, Edoardo Ragusa, Paolo Gastaldo +1 more
Traffic classification (TC) plays a critical role in cybersecurity, particularly in IoT and embedded contexts, where inspection must often occur...
Zixia Wang, Gaojie Jin, Jia Hu +1 more
Recent advancements in Large Language Models (LLMs) have led to their widespread adoption in daily applications. Despite their impressive...
Alexander Boyd, Franz Nowak, David Hyland +2 more
World models have been recently proposed as sandbox environments in which AI agents can be trained and evaluated before deployment. Although...
Aaron Sandoval, Cody Rushing
The field of AI Control seeks to develop robust control protocols, deployment safeguards for untrusted AI which may be intentionally subversive....
Adeela Bashir, The Anh han, Zia Ush Shamszaman
The integration of large language models (LLMs) into healthcare IoT systems promises faster decisions and improved medical support. LLMs are also...
Rongzhe Wei, Peizhi Niu, Xinjie Shen +7 more
Large language models (LLMs) remain vulnerable to jailbreak attacks that bypass safety guardrails to elicit harmful outputs. Existing approaches...
Xinyun Zhou, Xinfeng Li, Yinan Peng +9 more
Retrieval-Augmented Generation (RAG) systems are increasingly central to robust AI, enhancing large language model (LLM) faithfulness by...
Mihai Christodorescu, Earlence Fernandes, Ashish Hooda +11 more
In recent years, agentic artificial intelligence (AI) systems are becoming increasingly widespread. These systems allow agents to use various tools,...
Qingyuan Fei, Xin Liu, Song Li +4 more
Researchers have proposed numerous methods to detect vulnerabilities in JavaScript, especially those assisted by Large Language Models (LLMs)....
K. J. Kevin Feng, Tae Soo Kim, Rock Yuren Pang +3 more
AI agents that take actions in their environment autonomously over extended time horizons require robust governance interventions to curb their...
Yongyu Wang
Graph Neural Networks (GNNs) have emerged as a dominant paradigm for learning on graph-structured data, thanks to their ability to jointly exploit...
Yining Yuan, Yifei Wang, Yichang Xu +3 more
This paper presents LLMBugScanner, a large language model (LLM) based framework for smart contract vulnerability detection using fine-tuning and...
Kai Williams, Rohan Subramani, Francis Rhys Ward
Frontier AI developers may fail to align or control highly-capable AI agents. In many cases, it could be useful to have emergency shutdown mechanisms...
Henry Onyeka, Emmanuel Samson, Liang Hong +3 more
The increasing complexity of IoT edge networks presents significant challenges for anomaly detection, particularly in identifying sophisticated...
Aayush Garg, Zanis Ali Khan, Renzo Degiovanni +1 more
Automated vulnerability patching is crucial for software security, and recent advancements in Large Language Models (LLMs) present promising...
Neemesh Yadav, Francesco Ortu, Jiarui Liu +5 more
Large Language Models (LLMs) are trained to refuse to respond to harmful content. However, systematic analyses of whether this behavior is truly a...
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