2,529+ academic papers on AI security, attacks, and defenses
Showing 81–100 of 625 papers
Clear filtersQingyang 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...
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...
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...
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...
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...
Haochuan Kevin Wang
We present a stage-decomposed analysis of prompt injection attacks against five frontier LLM agents. Prior work measures task-level attack success...
Eyal Hadad, Mordechai Guri
On-device Vision-Language Models (VLMs) promise data privacy via local execution. However, we show that the architectural shift toward Dynamic...
Younes Salmi, Hanna Bogucka
Deep learning (DL) has been widely studied for assisting applications of modern wireless communications. One of the applications is automatic...
Younes Salmi, Hanna Bogucka
Deep Learning (DL) has become a key technology that assists radio frequency (RF) signal classification applications, such as modulation...
Younes Salmi, Hanna Bogucka
This paper investigates the susceptibility to model integrity attacks that overload virtual machines assigned by the k-means algorithm used for...
Hieu Xuan Le, Benjamin Goh, Quy Anh Tang
Prompt attacks, including jailbreaks and prompt injections, pose a critical security risk to Large Language Model (LLM) systems. In production,...
Haozhen Wang, Haoyue Liu, Jionghao Zhu +3 more
Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of applications. However, their practical deployment is...
Alexander Panfilov, Peter Romov, Igor Shilov +3 more
LLM agents like Claude Code can not only write code but also be used for autonomous AI research and engineering \citep{rank2026posttrainbench,...
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