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 61–80 of 109 papers
Clear filtersQi Li, Xinchao Wang
Enabling large language models (LLMs) to solve complex reasoning tasks is a key step toward artificial general intelligence. Recent work augments...
Narek Maloyan, Dmitry Namiot
The Model Context Protocol (MCP) has emerged as a de facto standard for integrating Large Language Models with external tools, yet no formal security...
Adeyemi Adeseye, Aisvarya Adeseye
Loop vulnerabilities are one major risky construct in software development. They can easily lead to infinite loops or executions, exhaust resources,...
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...
Junda Lin, Zhaomeng Zhou, Zhi Zheng +4 more
LLM agents operating in open environments face escalating risks from indirect prompt injection, particularly within the tool stream where manipulated...
Jingxiao Yang, Ping He, Tianyu Du +2 more
Recent advances in software vulnerability detection have been driven by Language Model (LM)-based approaches. However, these models remain vulnerable...
Zhaoqi Wang, Zijian Zhang, Daqing He +5 more
Large language models (LLMs) have demonstrated remarkable capabilities across diverse applications, however, they remain critically vulnerable to...
Keerthi Kumar. M, Swarun Kumar Joginpelly, Sunil Khemka +2 more
Background: Cyber-attacks have evolved rapidly in recent years, many individuals and business owners have been affected by cyber-attacks in various...
Qiang Yu, Xinran Cheng, Chuanyi Liu
As LLM agents transition from digital assistants to physical controllers in autonomous systems and robotics, they face an escalating threat from...
Hongming Fei, Zilong Hu, Prosanta Gope +1 more
Physical Unclonable Functions (PUFs) serve as lightweight, hardware-intrinsic entropy sources widely deployed in IoT security applications. However,...
Yunhao Feng, Yige Li, Yutao Wu +6 more
Large language model (LLM) agents execute tasks through multi-step workflows that combine planning, memory, and tool use. While this design enables...
Xiangdong Hu, Yangyang Jiang, Qin Hu +1 more
Multimodal Large Language Models (MLLMs) have become widely deployed, yet their safety alignment remains fragile under adversarial inputs. Previous...
Xin Wang, Yunhao Chen, Juncheng Li +7 more
The rapid integration of Multimodal Large Language Models (MLLMs) into critical applications is increasingly hindered by persistent safety...
Yueyan Dong, Minghui Xu, Qin Hu +5 more
Low-Rank Adaptation (LoRA) has become a popular solution for fine-tuning large language models (LLMs) in federated settings, dramatically reducing...
Toqeer Ali Syed, Mishal Ateeq Almutairi, Mahmoud Abdel Moaty
Powerful autonomous systems, which reason, plan, and converse using and between numerous tools and agents, are made possible by Large Language Models...
Songze Li, Jiameng Cheng, Yiming Li +2 more
By integrating language understanding with perceptual modalities such as images, multimodal large language models (MLLMs) constitute a critical...
Zehao Liu, Xi Lin
Large Language Models (LLMs) have gained considerable popularity and protected by increasingly sophisticated safety mechanisms. However, jailbreak...
Xiao Li, Yue Li, Hao Wu +4 more
As large language models (LLMs) are increasingly adopted for code vulnerability detection, their reliability and robustness across diverse...
Jinghao Wang, Ping Zhang, Carter Yagemann
Medical Large Language Models (LLMs) are increasingly deployed for clinical decision support across diverse specialties, yet systematic evaluation of...
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.
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