2,529+ academic papers on AI security, attacks, and defenses
Showing 21–40 of 729 papers
Clear filtersBikrant Bikram Pratap Maurya, Nitin Choudhury, Daksh Agarwal +1 more
Acoustic side-channel attacks (ASCA) on keyboards pose a significant security risk, as keystrokes can be inferred from typing acoustics, revealing...
Zuoyu Zhang, Yancheng Zhu
Tool-using agent systems powered by large language models (LLMs) are increasingly deployed across web, app, operating-system, and transactional...
Yuhui Wang, Tanqiu Jiang, Jiacheng Liang +2 more
As large language model (LLM)-powered agents are increasingly deployed to perform complex, real-world tasks, they face a growing class of attacks...
Zahir Alsulaimawi, Huaping Liu
Federated learning (FL) lets distributed nodes train a shared model without exchanging their raw data, but in privacy-sensitive deployments medical...
Gelei Xu, Ningzhi Tang, Xueyang Li +4 more
Healthcare automation is shaped by local procedures and organizational constraints, so agent capabilities rarely transfer unchanged across settings....
Judith Sáinz-Pardo Díaz, Álvaro López García
The growing development of artificial intelligence based solutions, together with privacy legislation, has driven the rise of the so-called privacy...
Karima Makhlouf, Lamiaa Basyoni, Syed Khaderi +4 more
Large language models (LLMs) are increasingly deployed in interactive and retrieval-augmented settings, raising significant privacy concerns. While...
Debeshee Das, Julien Piet, Darya Kaviani +3 more
Memory systems enable otherwise-stateless LLM agents to persist user information across sessions, but also introduce a new attack surface. We...
Zhiyang Dai, Yansong Gao, Boyu Kuang +5 more
Contrastive learning (CL) reduces annotation cost via auto-derived supervisory signals. Since large-scale in-house CL datasets are infeasible,...
Huining Cui, Wei Liu
Retrieval-augmented generation (RAG) improves factual grounding by conditioning large language models on retrieved evidence, but it also opens a...
Zehui Tang, Yuchen Liu, Feihu Huang
Federated learning (FL) is a popular distributed learning paradigm in machine learning, which enables multiple clients to collaboratively train...
Ermanno Francesco Sannini, Francesco Salzano, Simone Scalabrino +4 more
Smart Contracts are essential blockchain components, mainly written in Solidity. The high availability of public Solidity code leads to frequent...
Zhijun Li, Minghui Xu, Huayi Qi +6 more
Retrieval-Augmented Generation (RAG) is essential for enhancing Large Language Models (LLMs) with external knowledge, but its reliance on cloud...
Zhijun Li, Minghui Xu, Huayi Qi +6 more
Retrieval-Augmented Generation (RAG) is essential for enhancing Large Language Models (LLMs) with external knowledge, but its reliance on cloud...
Kemal Bicakci
Public agencies are beginning to consider large language models (LLMs) as decision-support tools for grant evaluation. This creates a practical...
Runze Cui, Fangxin Shang, Yehui Yang +2 more
Document understanding is a critical capability in financial credit review, onboarding, and remote verification, where both decision accuracy and...
Yuanfan Li, Qi Zhou, Chengzhengxu Li +5 more
We present MGTEVAL, an extensible platform for systematic evaluation of Machine-Generated Text (MGT) detectors. Despite rapid progress in MGT...
Aaron J. Li, Nicolas Sanchez, Hao Huang +8 more
Large language models (LLMs) are increasingly deployed, yet their outputs can be highly sensitive to routine, non-adversarial variation in how users...
German Marin, Jatin Chaudhary
Autonomous AI agents can remain fully authorized and still become unsafe as behavior drifts, adversaries adapt, and decision patterns shift without...
Qi Li, Jiu Li, Pingtao Wei +8 more
This report presents a comparative evaluation of DKnownAI Guard in AI agent security scenarios, benchmarked against three competing products: AWS...
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