2,077+ academic papers on AI security, attacks, and defenses
Showing 761–780 of 809 papers
Clear filtersChenxiang Luo, David K. Y. Yau, Qun Song
Federated learning (FL) enables collaborative model training without sharing raw data but is vulnerable to gradient inversion attacks (GIAs), where...
Dalal Alharthi, Ivan Roberto Kawaminami Garcia
Large language models have gained widespread prominence, yet their vulnerability to prompt injection and other adversarial attacks remains a critical...
Samar Fares, Nurbek Tastan, Noor Hussein +1 more
Generative models can generate photorealistic images at scale. This raises urgent concerns about the ability to detect synthetically generated images...
Luis Burbano, Diego Ortiz, Qi Sun +5 more
Embodied Artificial Intelligence (AI) promises to handle edge cases in robotic vehicle systems where data is scarce by using common-sense reasoning...
Anshul Nasery, Edoardo Contente, Alkin Kaz +2 more
Model fingerprinting has emerged as a promising paradigm for claiming model ownership. However, robustness evaluations of these schemes have mostly...
Firas Ben Hmida, Abderrahmen Amich, Ata Kaboudi +1 more
Deep neural networks (DNNs) are increasingly being deployed in high-stakes applications, from self-driving cars to biometric authentication. However,...
Shuai Shao, Qihan Ren, Chen Qian +8 more
Advances in Large Language Models (LLMs) have enabled a new class of self-evolving agents that autonomously improve through interaction with the...
Qinjian Zhao, Jiaqi Wang, Zhiqiang Gao +3 more
Large Language Models (LLMs) have achieved impressive performance across diverse natural language processing tasks, but their growing power also...
Xiaobao Wang, Ruoxiao Sun, Yujun Zhang +4 more
Graph Neural Networks (GNNs) have demonstrated strong performance across tasks such as node classification, link prediction, and graph...
Marco Zimmerli, Andreas Plesner, Till Aczel +1 more
Deep neural networks remain vulnerable to adversarial examples despite advances in architectures and training paradigms. We investigate how training...
Dennis Jacob, Emad Alghamdi, Zhanhao Hu +2 more
Large language models (LLMs) have become increasingly popular due to their ability to interact with unstructured content. As such, LLMs are now a key...
Yein Park, Jungwoo Park, Jaewoo Kang
Large language models (LLMs), despite being safety-aligned, exhibit brittle refusal behaviors that can be circumvented by simple linguistic changes....
Yuepeng Hu, Zhengyuan Jiang, Mengyuan Li +4 more
Large language models (LLMs) are often modified after release through post-processing such as post-training or quantization, which makes it...
Yanchen Jiang, Zhe Feng, Aranyak Mehta
Large language models (LLMs) are increasingly used in modern search and answer systems to synthesize multiple, sometimes conflicting, texts into a...
Yupei Liu, Yanting Wang, Yuqi Jia +2 more
Prompt injection attacks pose a pervasive threat to the security of Large Language Models (LLMs). State-of-the-art prevention-based defenses...
Thomas Fargues, Ye Dong, Tianwei Zhang +1 more
The rapid growth of Large Language Models (LLMs) has highlighted the pressing need for reliable mechanisms to verify content ownership and ensure...
Zhifang Zhang, Qiqi Tao, Jiaqi Lv +3 more
Large vision-language models (LVLMs) have achieved impressive performance across a wide range of vision-language tasks, while they remain vulnerable...
Yuzhen Long, Songze Li
Autonomous driving systems increasingly rely on multi-agent architectures powered by large language models (LLMs), where specialized agents...
Jongwook Han, Jongwon Lim, Injin Kong +1 more
Large language models can express values in two main ways: (1) intrinsic expression, reflecting the model's inherent values learned during training,...
Zihao Zhu, Xinyu Wu, Gehan Hu +3 more
Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in complex problem-solving through Chain-of-Thought (CoT) reasoning. However,...
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