AI Threat Alert indexes 4,319+ 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 3121–3140 of 4,319 papers
Shawn Li, Chenxiao Yu, Zhiyu Ni +4 more
Large language models (LLMs) are increasingly deployed in security-sensitive applications, where they must follow system- or developer-specified...
Mingxiang Tao, Yu Tian, Wenxuan Tu +3 more
Federated learning (FL) addresses data privacy and silo issues in large language models (LLMs). Most prior work focuses on improving the training...
Yixiao Peng, Hao Hu, Feiyang Li +5 more
While virtualization and resource pooling empower cloud networks with structural flexibility and elastic scalability, they inevitably expand the...
Andrew D. Maynard
Large language model (LLM)-based conversational AI systems present a challenge to human cognition that current frameworks for understanding...
Hongyan Chang, Ergute Bao, Xinjian Luo +1 more
Large language models (LLMs) increasingly rely on retrieving information from external corpora. This creates a new attack surface: indirect prompt...
Harshil Parmar, Pushti Vyas, Prayers Khristi +1 more
As vulnerability research increasingly adopts generative AI, a critical reliance on opaque model outputs has emerged, creating a "trust gap" in...
Ying Zhou, Jiacheng Wei, Yu Qi +2 more
Large language models (LLMs) demonstrate remarkable capabilities in natural language understanding and generation. Despite being trained on...
Huihui Huang, Jieke Shi, Junkai Chen +6 more
Penetration testing is essential for identifying vulnerabilities in web applications before real adversaries can exploit them. Recent work has...
Masahiro Kaneko
The use of large language models (LLMs) in peer review systems has attracted growing attention, making it essential to examine their potential...
Takaaki Toda, Tatsuya Mori
Modern software package registries like PyPI have become critical infrastructure for software development, but are increasingly exploited by threat...
Vasanth Iyer, Leonardo Bobadilla, S. S. Iyengar
Large Language Models (LLMs) such as Gemma-2B have shown strong performance in various natural language processing tasks. However, general-purpose...
Muhammad Wahid Akram, Keshav Sood, Muneeb Ul Hassan +1 more
Phishing with Quick Response (QR) codes is termed as Quishing. The attackers exploit this method to manipulate individuals into revealing their...
Abdulhadi Shoufan, Ahmad-Azmi-Abdelhamid Esmaeil
As students increasingly rely on large language models, hallucinations pose a growing threat to learning. To mitigate this, AI literacy must expand...
Kaiwen Zhou, Shreedhar Jangam, Ashwin Nagarajan +7 more
Large language model-based agents are rapidly evolving from simple conversational assistants into autonomous systems capable of performing complex,...
Quan Minh Nguyen, Min-Seon Kim, Hoang M. Ngo +3 more
Membership inference attack (MIA) poses a significant privacy threat in federated learning (FL) as it allows adversaries to determine whether a...
Qiang Zhang, Elena Emma Wang, Jiaming Li +1 more
This study presents a Secure Multi-Tenant Architecture (SMTA) combined with a novel concept Burn-After-Use (BAU) mechanism for enterprise LLM...
Chalitha Handapangoda
The reliance of Large Language Models and Internet of Things systems on massive, globally distributed data flows creates systemic security and...
Hongjun An, Yiliang Song, Jiangan Chen +3 more
Large Language Model (LLM) training often optimizes for preference alignment, rewarding outputs that are perceived as helpful and...
Imtiaz Ali Soomro, Hamood Ur Rehman, S. Jawad Hussain ID +3 more
The rapid proliferation of Internet of Things (IoT) devices across domains such as smart homes, industrial control systems, and healthcare networks...
Chao Liu, Ngai-Man Cheung
3D Vision-Language Models (VLMs), such as PointLLM and GPT4Point, have shown strong reasoning and generalization abilities in 3D understanding tasks....
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 4,319+ 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.
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