2,583+ academic papers on AI security, attacks, and defenses
Showing 461–480 of 1,933 papers
Clear filtersAiman Almasoud, Antony Anju, Marco Arazzi +6 more
LLM-as-a-Judge (LaaJ) is a novel paradigm in which powerful language models are used to assess the quality, safety, or correctness of generated...
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...
Miles Farmer, Ekincan Ufuktepe, Anne Watson +4 more
Large Language Models (LLMs) have emerged as a popular choice in vulnerability detection studies given their foundational capabilities, open source...
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...
KrishnaSaiReddy Patil
LLM-based chatbots in government services face critical security gaps. Multi-turn adversarial attacks achieve over 90% success against current...
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....
Yubo Li, Lu Zhang, Tianchong Jiang +2 more
Large language models systematically fail when a salient surface cue conflicts with an unstated feasibility constraint. We study this through a...
Yicheng Cai, Mitchell John DeStefano, Guodong Dong +5 more
As Large Language Models (LLMs) and multi-agent AI systems are demonstrating increasing potential in cybersecurity operations, organizations,...
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...
Osama Wehbi, Sarhad Arisdakessian, Omar Abdel Wahab +3 more
Backdoor attacks pose a significant threat to the integrity and reliability of Artificial Intelligence (AI) models, enabling adversaries to...
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...
Zihao Xu, Xiao Cheng, Ruijie Meng +1 more
LLM API calls are becoming a ubiquitous program construct, yet they create a boundary that no existing program analysis can cross: runtime values...
Aymen Lassoued, Nacef Mbarek, Bechir Dardouri +3 more
Vulnerability detection in C programs is a critical challenge in software security. Although large language models (LLMs) achieve strong detection...
Quan Zhang, Lianhang Fu, Lvsi Lian +5 more
Equipping LLM agents with real-world tools can substantially improve productivity. However, granting agents autonomy over tool use also transfers the...
Tran Duong Minh Dai, Triet Huynh Minh Le, M. Ali Babar +2 more
Although Graph Neural Networks (GNNs) have shown promise for smart contract vulnerability detection, they still face significant limitations....
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...
Ruiyang Wang, Rong Pan, Zhengan Yao
Federated learning (FL) enables distributed clients to collaboratively train a global model using local private data. Nevertheless, recent studies...
Kesheng Chen, Yamin Hu, Qi Zhou +2 more
Vision-language models (VLMs) achieve strong performance on many benchmarks, yet a basic reliability question remains underexplored: when visual...
Bhavuk Jain, Sercan Ö. Arık, Hardeo K. Thakur
Multimodal large language models (MLLMs) integrate information from multiple modalities such as text, images, audio, and video, enabling complex...
Vishal Narnaware, Animesh Gupta, Kevin Zhai +2 more
Multimodal Diffusion Large Language Models (MDLLMs) achieve high-concurrency generation through parallel masked decoding, yet the architectures...
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