2,077+ academic papers on AI security, attacks, and defenses
Showing 361–380 of 603 papers
Clear filtersFeijiang Han
Malicious WebShells pose a significant and evolving threat by compromising critical digital infrastructures and endangering public services in...
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...
Songwen Zhao, Danqing Wang, Kexun Zhang +3 more
Vibe coding is a new programming paradigm in which human engineers instruct large language model (LLM) agents to complete complex coding tasks with...
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...
Han Luo, Guy Laban
Large language models (LLMs) now mediate many web-based mental-health, crisis, and other emotionally sensitive services, yet their psychosocial...
Zahra Mahdavi, Zahra Khodakaramimaghsoud, Hooman Khaloo +4 more
Large vision-language models (LVLMs) are now central to healthcare applications such as medical visual question answering and imaging report...
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)....
Jianxiang Zang, Yongda Wei, Ruxue Bai +5 more
Reliable reward models (RMs) are critical for ensuring the safe alignment of large language models (LLMs). However, current RM evaluation methods...
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...
Jiawei Chen, Yang Yang, Chao Yu +6 more
Large Reasoning Models (LRMs) have emerged as a powerful advancement in multi-step reasoning tasks, offering enhanced transparency and logical...
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...
Peng Kuang, Xiangxiang Wang, Wentao Liu +2 more
Multimodal Large Language Models (MLLMs) have achieved impressive performances in mathematical reasoning, yet they remain vulnerable to visual...
Anudeex Shetty
Large Language Models (LLMs) have demonstrated exceptional capabilities in natural language understanding and generation. Based on these LLMs,...
Abeer Matar A. Almalky, Ziyan Wang, Mohaiminul Al Nahian +2 more
In recent years, large language models (LLMs) have achieved substantial advancements and are increasingly integrated into critical applications...
Mohaiminul Al Nahian, Abeer Matar A. Almalky, Gamana Aragonda +6 more
Adversarial weight perturbation has emerged as a concerning threat to LLMs that either use training privileges or system-level access to inject...
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