2,583+ academic papers on AI security, attacks, and defenses
Showing 2141–2160 of 2,583 papers
Lu Liu, Wuqi Zhang, Lili Wei +3 more
Decentralized Finance (DeFi) smart contracts manage billions of dollars, making them a prime target for exploits. Price manipulation vulnerabilities,...
Christoph Bühler, Matteo Biagiola, Luca Di Grazia +1 more
Large Language Models (LLMs) have evolved into AI agents that interact with external tools and environments to perform complex tasks. The Model...
Xingwei Zhong, Kar Wai Fok, Vrizlynn L. L. Thing
Multimodal large language models (MLLMs) comprise of both visual and textual modalities to process vision language tasks. However, MLLMs are...
Mingrui Liu, Sixiao Zhang, Cheng Long +1 more
As Large Language Models (LLMs) become integral to computing infrastructure, safety alignment serves as the primary security control preventing the...
Yukun Jiang, Mingjie Li, Michael Backes +1 more
Despite their superior performance on a wide range of domains, large language models (LLMs) remain vulnerable to misuse for generating harmful...
Divyanshu Kumar, Nitin Aravind Birur, Tanay Baswa +2 more
Frontier Large Language Models (LLMs) pose unprecedented dual-use risks through the potential proliferation of chemical, biological, radiological,...
Nils Philipp Walter, Chawin Sitawarin, Jamie Hayes +2 more
Large Language Models (LLMs) are increasingly deployed in agentic systems that interact with an external environment; this makes them susceptible to...
Li An, Yujian Liu, Yepeng Liu +3 more
Watermarking has emerged as a promising solution for tracing and authenticating text generated by large language models (LLMs). A common approach to...
Nguyen Linh Bao Nguyen, Alsharif Abuadbba, Kristen Moore +1 more
The rapid advancement of generative models has enabled the creation of increasingly stealthy synthetic voices, commonly referred to as audio...
Zheng-Xin Yong, Stephen H. Bach
We discover a novel and surprising phenomenon of unintentional misalignment in reasoning language models (RLMs), which we call self-jailbreaking....
Soham Hans, Stacy Marsella, Sophia Hirschmann +1 more
Understanding adversarial behavior in cybersecurity has traditionally relied on high-level intelligence reports and manual interpretation of attack...
Austin Jia, Avaneesh Ramesh, Zain Shamsi +2 more
Retrieval-Augmented Generation (RAG) has emerged as the dominant architectural pattern to operationalize Large Language Model (LLM) usage in Cyber...
Antônio H. Ribeiro, David Vävinggren, Dave Zachariah +2 more
Adversarial training has emerged as a key technique to enhance model robustness against adversarial input perturbations. Many of the existing methods...
Ronghao Ni, Aidan Z. H. Yang, Min-Chien Hsu +5 more
Program analysis tools often produce large volumes of candidate vulnerability reports that require costly manual review, creating a practical...
Alyssa Gerhart, Balaji Iyangar
Adversarial attacks pose a severe risk to AI systems used in healthcare, capable of misleading models into dangerous misclassifications that can...
Wei Shao, Yuhao Wang, Rongguang He +2 more
Existing defence mechanisms have demonstrated significant effectiveness in mitigating rule-based Denial-of-Service (DoS) attacks, leveraging...
Mohamed Seif, Malcolm Egan, Andrea J. Goldsmith +1 more
AI-based sensing at wireless edge devices has the potential to significantly enhance Artificial Intelligence (AI) applications, particularly for...
Zhenghao Xu, Qin Lu, Qingru Zhang +9 more
Reward model (RM) plays a pivotal role in reinforcement learning with human feedback (RLHF) for aligning large language models (LLMs). However,...
Daniel Gilkarov, Ran Dubin
Pretrained deep learning model sharing holds tremendous value for researchers and enterprises alike. It allows them to apply deep learning by...
Chiyu Chen, Xinhao Song, Yunkai Chai +7 more
Vision-Language Models (VLMs) are increasingly deployed as autonomous agents to navigate mobile graphical user interfaces (GUIs). Operating in...
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