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 201–218 of 218 papers
Clear filtersJingxiao 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,...
Zhilun Zhou, Zihan Liu, Jiahe Liu +5 more
Large Language Model-based Multi-Agent Systems (LLM-based MAS), where multiple LLM agents collaborate to solve complex tasks, have shown impressive...
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...
Rasmus Erlemann, Charles Colyer Morris, Sanjyot Sathe
The emergence of large-scale quantum computing threatens widely deployed public-key cryptographic systems, creating an urgent need for...
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...
Sofie Goethals, Foster Provost, João Sedoc
As generative AI systems become integrated into real-world applications, organizations increasingly need to be able to understand and interpret their...
Gongao Zhang, Haijiang Zeng, Lu Jiang
Financial institutions and regulators require systems that integrate heterogeneous data to assess risks from stock fluctuations to systemic...
Md Ajoad Hasan, Dipayan Saha, Khan Thamid Hasan +5 more
The growing complexity of modern system-on-chip (SoC) and IP designs is making security assurance difficult day by day. One of the fundamental steps...
Arina Kharlamova, Youcheng Sun, Ting Yu
Private macOS frameworks underpin critical services and daemons but remain undocumented and distributed only as stripped binaries, complicating...
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...
Vidyut Sriram, Sawan Pandita, Achintya Lakshmanan +2 more
Large Language Models (LLMs) can generate code but often introduce security vulnerabilities, logical inconsistencies, and compilation errors. Prior...
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...
Armstrong Foundjem, Lionel Nganyewou Tidjon, Leuson Da Silva +1 more
Machine learning (ML) underpins foundation models in finance, healthcare, and critical infrastructure, making them targets for data poisoning, model...
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