2,077+ academic papers on AI security, attacks, and defenses
Showing 41–60 of 522 papers
Clear filtersPratyay Kumar, Abu Saleh Md Tayeen, Satyajayant Misra +4 more
Deep learning (DL)-based Network Intrusion Detection System (NIDS) has demonstrated great promise in detecting malicious network traffic. However,...
David Fernandez, Pedram MohajerAnsari, Amir Salarpour +3 more
Vision-language models are emerging for autonomous driving, yet their robustness to physical adversarial attacks remains unexplored. This paper...
Junxian Li, Tu Lan, Haozhen Tan +2 more
Modern vision-language-model (VLM) based graphical user interface (GUI) agents are expected not only to execute actions accurately but also to...
Yonghong Deng, Zhen Yang, Ping Jian +3 more
With the rapid advancement of large language models (LLMs), the safety of LLMs has become a critical concern. Despite significant efforts in safety...
Jialai Wang, Ya Wen, Zhongmou Liu +4 more
Targeted bit-flip attacks (BFAs) exploit hardware faults to manipulate model parameters, posing a significant security threat. While prior work...
Ondřej Lukáš, Jihoon Shin, Emilia Rivas +6 more
Autonomous offensive agents often fail to transfer beyond the networks on which they are trained. We isolate a minimal but fundamental shift --...
Jinman Wu, Yi Xie, Shiqian Zhao +1 more
Currently, open-sourced large language models (OSLLMs) have demonstrated remarkable generative performance. However, as their structure and weights...
Yuanbo Li, Tianyang Xu, Cong Hu +3 more
The rapid progress of Multi-Modal Large Language Models (MLLMs) has significantly advanced downstream applications. However, this progress also...
Yuanbo Li, Tianyang Xu, Cong Hu +3 more
The rapid progress of Multi-Modal Large Language Models (MLLMs) has significantly advanced downstream applications. However, this progress also...
Junchen Li, Chao Qi, Rongzheng Wang +5 more
Retrieval-Augmented Generation (RAG) enhances the capabilities of large language models (LLMs) by incorporating external knowledge, but its reliance...
Wang Jian, Shen Hong, Ke Wei +1 more
While federated learning protects data privacy, it also makes the model update process vulnerable to long-term stealthy perturbations. Existing...
Yangyang Wei, Yijie Xu, Zhenyuan Li +2 more
Multi-Agent System is emerging as the \textit{de facto} standard for complex task orchestration. However, its reliance on autonomous execution and...
Neha Nagaraja, Lan Zhang, Zhilong Wang +2 more
Multimodal Large Language Models (MLLMs) integrate vision and text to power applications, but this integration introduces new vulnerabilities. We...
Zhi Xu, Jiaqi Li, Xiaotong Zhang +2 more
Large language models (LLMs) have achieved remarkable success across diverse applications but remain vulnerable to jailbreak attacks, where attackers...
Peter Horvath, Ilia Shumailov, Lukasz Chmielewski +2 more
The multi-million dollar investment required for modern machine learning (ML) has made large ML models a prime target for theft. In response, the...
Jiayao Wang, Mohammad Maruf Hasan, Yiping Zhang +5 more
Self-Supervised Learning (SSL) has emerged as a significant paradigm in representation learning thanks to its ability to learn without extensive...
Huw Day, Adrianna Jezierska, Jessica Woodgate
Large Language Models have intensified the scale and strategic manipulation of political discourse on social media, leading to conflict escalation....
Duoxun Tang, Dasen Dai, Jiyao Wang +3 more
Video-LLMs are increasingly deployed in safety-critical applications but are vulnerable to Energy-Latency Attacks (ELAs) that exhaust computational...
Xinyu Huang, Qiang Yang, Leming Shen +2 more
Embodied Large Language Models (LLMs) enable AI agents to interact with the physical world through natural language instructions and actions....
Jiayao Wang, Yiping Zhang, Mohammad Maruf Hasan +5 more
Self-supervised diffusion models learn high-quality visual representations via latent space denoising. However, their representation layer poses a...
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