2,560+ academic papers on AI security, attacks, and defenses
Showing 281–300 of 442 papers
Clear filtersAink 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...
Qiwei Tian, Chenhao Lin, Zhengyu Zhao +1 more
To address the trade-off between robustness and performance for robust VLM, we observe that function words could incur vulnerability of VLMs against...
Cheng Cheng, Jinqiu Yang
Code-focused Large Language Models (LLMs), such as CodeX and Star-Coder, have demonstrated remarkable capabilities in enhancing developer...
Ashish Hooda, Mihai Christodorescu, Chuangang Ren +3 more
Machine learning (ML) models for code clone detection determine whether two pieces of code are semantically equivalent, which in turn is a key...
Chenlin Xu, Lei Zhang, Lituan Wang +5 more
Due to the scarcity of annotated data and the substantial computational costs of model, conventional tuning methods in medical image segmentation...
Yizhou Zhao, Zhiwei Steven Wu, Adam Block
Watermarking aims to embed hidden signals in generated text that can be reliably detected when given access to a secret key. Open-weight language...
Tengyun Ma, Jiaqi Yao, Daojing He +4 more
Large Language Models (LLMs) have emerged as powerful tools for diverse applications. However, their uniform token processing paradigm introduces...
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...
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...
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)....
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...
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...
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