2,583+ academic papers on AI security, attacks, and defenses
Showing 481–500 of 994 papers
Clear filtersMd Nabi Newaz Khan, Abdullah Arafat Miah, Yu Bi
Graph neural network (GNN) have demonstrated exceptional performance in solving critical problems across diverse domains yet remain susceptible to...
Sahar Tahmasebi, Eric Müller-Budack, Ralph Ewerth
Misinformation and fake news have become a pressing societal challenge, driving the need for reliable automated detection methods. Prior research has...
Piyumi Bhagya Sudasinghe, Kushan Sudheera Kalupahana Liyanage, Harsha S. Gardiyawasam Pussewalage
The rapid growth of Internet of Things (IoT) devices has increased the scale and diversity of cyberattacks, exposing limitations in traditional...
Zhihao Chen, Zirui Gong, Jianting Ning +2 more
Federated Rank Learning (FRL) is a promising Federated Learning (FL) paradigm designed to be resilient against model poisoning attacks due to its...
Víctor Mayoral-Vilches, Stefan Rass, Martin Pinzger +14 more
Cybersecurity superintelligence -- artificial intelligence exceeding the best human capability in both speed and strategic reasoning -- represents...
Haodong Chen, Ziheng Zhang, Jinghui Jiang +2 more
Cloud environments face frequent DDoS threats due to centralized resources and broad attack surfaces. Modern cloud-native DDoS attacks further evolve...
Andrew Crossman, Jonah Dodd, Viralam Ramamurthy Chaithanya Kumar +5 more
MITRE ATT&CK is a cybersecurity knowledge base that organizes threat actor and cyber-attack information into a set of tactics describing the reasons...
Mohammad Shamim Ahsan, Peng Liu
In the network security domain, due to practical issues -- including imbalanced data and heterogeneous legitimate network traffic -- adversarial...
Zhihao Dou, Dongfei Cui, Weida Wang +7 more
Split Learning (SL) offers a framework for collaborative model training that respects data privacy by allowing participants to share the same dataset...
Xiaochen Zhu, Mayuri Sridhar, Srinivas Devadas
Modern machine learning models are increasingly deployed behind APIs. This renders standard weight-privatization methods (e.g. DP-SGD) unnecessarily...
Yilin Tang, Yu Wang, Lanlan Qiu +4 more
Large language models (LLMs) have shown strong capabilities in multi-step decision-making, planning and actions, and are increasingly integrated into...
Rishit Chugh
The deployment of large language models (LLMs) has raised security concerns due to their susceptibility to producing harmful or policy-violating...
Jiani Liu, Yixin He, Lanlan Fan +5 more
Navigation agents powered by large language models (LLMs) convert natural language instructions into executable plans and actions. Compared to...
Bingxin Xu, Yuzhang Shang, Binghui Wang +1 more
Vision-Language-Action (VLA) models are increasingly deployed in safety-critical robotic applications, yet their security vulnerabilities remain...
Asen Dotsinski, Panagiotis Eustratiadis
As open-weight large language models (LLMs) increase in capabilities, safeguarding them against malicious prompts and understanding possible attack...
Diego Gosmar, Deborah A. Dahl
Prompt injection remains a central obstacle to the safe deployment of large language models, particularly in multi-agent settings where intermediate...
Xiaolei Zhang, Xiaojun Jia, Liquan Chen +1 more
Introducing reasoning models into Retrieval-Augmented Generation (RAG) systems enhances task performance through step-by-step reasoning, logical...
Advije Rizvani, Giovanni Apruzzese, Pavel Laskov
Large Language Models (LLMs) are increasingly adopted in the financial domain. Their exceptional capabilities to analyse textual data make them...
Jesus-German Ortiz-Barajas, Jonathan Tonglet, Vivek Gupta +1 more
Multimodal large language models (MLLMs) are increasingly used to automate chart generation from data tables, enabling efficient data analysis and...
Murat Bilgehan Ertan, Emirhan Böge, Min Chen +2 more
As large language models (LLMs) are trained on increasingly opaque corpora, membership inference attacks (MIAs) have been proposed to audit whether...
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