2,560+ academic papers on AI security, attacks, and defenses
Showing 141–160 of 728 papers
Clear filtersZonghao Ying, Haowen Dai, Lianyu Hu +5 more
Modern text-to-image (T2I) models can now render legible, paragraph-length text, enabling a fundamentally new class of misuse. We identify and...
Yiyang Zhang, Chaojian Yu, Ziming Hong +4 more
Multimodal pretrained models are vulnerable to backdoor attacks, yet most existing methods rely on visual or multimodal triggers, which are...
Qingyang Xu, Yaling Shen, Stephanie Fong +7 more
The increasing use of large language models (LLMs) in mental healthcare raises safety concerns in high-stakes therapeutic interactions. A key...
Vinod Vaikuntanathan, Or Zamir
AI agents are increasingly deployed to interact with other agents on behalf of users and organizations. We ask whether two such agents, operated by...
Qiqing Huang, Xingyu Wang, Wanda Guo +2 more
Modern 5G user equipment (UE) processes Radio Resource Control (RRC) configuration messages during early control-plane exchanges, before...
Aobo Chen, Chenxu Zhao, Chenglin Miao +1 more
Large language models (LLMs) possess strong semantic understanding, driving significant progress in data mining applications. This is further...
Siyuan Li, Zehao Liu, Xi Lin +6 more
As Large Language Models (LLMs) are increasingly deployed in complex applications, their vulnerability to adversarial attacks raises urgent safety...
Vickson Ferrel
As TLS 1.3 encryption limits traditional Deep Packet Inspection (DPI), the security community has pivoted to Euclidean Transformer-based classifiers...
Zikai Zhang, Rui Hu, Olivera Kotevska +1 more
Large Language Models (LLMs) are powerful tools for answering user queries, yet they remain highly vulnerable to jailbreak attacks. Existing...
Tiankai Yang, Jiate Li, Yi Nian +5 more
LLM-based agents increasingly operate across repeated sessions, maintaining task states to ensure continuity. In many deployments, a single agent...
Yanting Wang, Wei Zou, Runpeng Geng +1 more
Large language models (LLMs) and their applications, such as agents, are highly vulnerable to prompt injection attacks. State-of-the-art prompt...
Jiaqing Li, Zhibo Zhang, Shide Zhou +3 more
Model merging has emerged as a powerful technique for combining specialized capabilities from multiple fine-tuned LLMs without additional training...
Quanyan Zhu, Zhengye Han
This paper introduces a performative scenario optimization framework for decision-dependent chance-constrained problems. Unlike classical stochastic...
Meiwen Ding, Song Xia, Chenqi Kong +1 more
Although multimodal large language models (MLLMs) are increasingly deployed in real-world applications, their instruction-following behavior leaves...
Yubo Cui, Xianchao Guan, Zijun Xiong +1 more
Pre-trained vision-language models (VLMs) exhibit strong zero-shot generalization but remain vulnerable to adversarial perturbations. Existing...
Kavindu Herath, Joshua Zhao, Saurabh Bagchi
Backdoor attacks on federated learning (FL) are most often evaluated with synthetic corner patches or out-of-distribution (OOD) patterns that are...
Yunrui Yu, Xuxiang Feng, Pengda Qin +5 more
Adversarial robustness evaluation faces a critical challenge as new defense paradigms emerge that can exploit limitations in existing assessment...
Bilgehan Sel, Xuanli He, Alwin Peng +2 more
Fine-tuning APIs offered by major AI providers create new attack surfaces where adversaries can bypass safety measures through targeted fine-tuning....
Chihan Huang, Huaijin Wang, Shuai Wang
The pervasive deployment of deep learning models across critical domains has concurrently intensified privacy concerns due to their inherent...
Chengyin Hu, Jiaju Han, Xuemeng Sun +6 more
Vision-language models (VLMs) rely on a shared visual-textual representation space to perform tasks such as zero-shot classification, image...
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