AI Threat Alert indexes 3,023+ peer-reviewed and preprint papers on AI/ML security — covering adversarial attacks, model defenses, red-teaming benchmarks, surveys, and security tooling. Papers are sourced from arXiv, classified by type and by relevance to real-world threats, and cross-referenced with the CVEs and incidents they relate to.
Showing 1001–1020 of 1,175 papers
Clear filtersWei Shao, Yuhao Wang, Rongguang He +2 more
Existing defence mechanisms have demonstrated significant effectiveness in mitigating rule-based Denial-of-Service (DoS) attacks, leveraging...
Daniel Gilkarov, Ran Dubin
Pretrained deep learning model sharing holds tremendous value for researchers and enterprises alike. It allows them to apply deep learning by...
Chiyu Chen, Xinhao Song, Yunkai Chai +7 more
Vision-Language Models (VLMs) are increasingly deployed as autonomous agents to navigate mobile graphical user interfaces (GUIs). Operating in...
Divyanshu Kumar, Shreyas Jena, Nitin Aravind Birur +3 more
Multimodal large language models (MLLMs) have achieved remarkable progress, yet remain critically vulnerable to adversarial attacks that exploit...
Tushar Nayan, Ziqi Zhang, Ruimin Sun
With the increasing deployment of Large Language Models (LLMs) on mobile and edge platforms, securing them against model extraction attacks has...
Mohamed ElShehaby, Ashraf Matrawy
Adversarial attacks pose significant challenges to Machine Learning (ML) systems and especially Deep Neural Networks (DNNs) by subtly manipulating...
Ariana Yi, Ce Zhou, Liyang Xiao +1 more
As object detection models are increasingly deployed in cyber-physical systems such as autonomous vehicles (AVs) and surveillance platforms, ensuring...
Jia Deng, Jin Li, Zhenhua Zhao +1 more
Vision-Language Models (VLMs), such as CLIP, have demonstrated remarkable zero-shot generalizability across diverse downstream tasks. However, recent...
Petar Radanliev
Problem Space: AI Vulnerabilities and Quantum Threats Generative AI vulnerabilities: model inversion, data poisoning, adversarial inputs. Quantum...
R. Can Aygun, Yehuda Afek, Anat Bremler-Barr +1 more
With the goal of improving the security of Internet protocols, we seek faster, semi-automatic methods to discover new vulnerabilities in protocols...
Yizhu Wang, Sizhe Chen, Raghad Alkhudair +2 more
When large language model (LLM) agents are increasingly deployed to automate tasks and interact with untrusted external data, prompt injection...
Sanskar Amgain, Daniel Lobo, Atri Chatterjee +2 more
The growing use of third-party hardware accelerators (e.g., FPGAs, ASICs) for deep neural networks (DNNs) introduces new security vulnerabilities....
Zheng Zhang, Jiarui He, Yuchen Cai +4 more
As large language model (LLM) agents increasingly automate complex web tasks, they boost productivity while simultaneously introducing new security...
Isaac Wu, Michael Maslowski
As large language models (LLMs) become integrated into various sensitive applications, prompt injection, the use of prompting to induce harmful...
Neeladri Bhuiya, Madhav Aggarwal, Diptanshu Purwar
Large Language Models (LLMs) are improving at an exceptional rate. With the advent of agentic workflows, multi-turn dialogue has become the de facto...
Xu Zhang, Hao Li, Zhichao Lu
Multimodal Large Language Models (MLLMs) achieve strong reasoning and perception capabilities but are increasingly vulnerable to jailbreak attacks....
Vincenzo Carletti, Pasquale Foggia, Carlo Mazzocca +2 more
Federated Learning (FL) enables collaborative training of Machine Learning (ML) models across multiple clients while preserving their privacy. Rather...
Yushi Yang, Shreyansh Padarha, Andrew Lee +1 more
Agentic reinforcement learning (RL) trains large language models to autonomously call tools during reasoning, with search as the most common...
Xinkai Wang, Beibei Li, Zerui Shao +3 more
Multimodal large language models (MLLMs) have become integral to a wide range of real-world applications by jointly reasoning over text and visual...
Giulia Giusti
The concept of linearity plays a central role in both mathematics and computer science, with distinct yet complementary meanings. In mathematics,...
AI security research studies how AI and machine-learning systems can be attacked and defended — covering adversarial examples, prompt injection, model poisoning, training-data extraction, and the mitigations against them. AI Threat Alert curates this research from academic sources so security teams can track the threats behind emerging AI risks.
AI Threat Alert indexes 3,023+ papers on AI/ML security, classified across attack, defense, benchmark, survey, and tool categories and updated continuously.
Papers are sourced from arXiv, then classified by type and by relevance to real-world AI/ML threats, and cross-referenced with the CVEs and incidents they relate to.
Coverage spans adversarial attacks, model and system defenses, red-teaming benchmarks, literature surveys, and security tooling for LLMs, ML libraries, AI agents, and inference pipelines.
Every paper is filtered for AI security relevance and linked to the vulnerabilities, vendors, and incidents it relates to, so the research connects directly to operational threat intelligence.
Get breaking CVE alerts, compliance reports (ISO 42001, EU AI Act), and CISO risk assessments for your AI/ML stack.
Start 14-Day Free Trial
AI Threat Alert