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 121–140 of 748 papers
Clear filtersZi Liang, Ronghua Li, Yanyun Wang +2 more
Large Language Model (LLM) agents have emerged as key intermediaries, orchestrating complex interactions between human users and a wide range of...
Elham Pourabbas Vafa, Sayak Saha Roy, Shirin Nilizadeh
We demonstrate how publicly available social-media data and generative AI (GenAI) can be misused to automate and scale highly personalized,...
Ali Karakoc, H. Birkan Yilmaz
SQL injection (SQLi) attacks are still one of the serious attacks ranked in the Open Worldwide Application Security Project (OWASP) Top 10 threats....
Mengqi He, Xinyu Tian, Xin Shen +6 more
Recent studies show that gradient-based universal image jailbreaks on vision-language models (VLMs) exhibit little or no cross-model transferability,...
Zheng Lin, Zhenxing Niu, Haoxuan Ji +2 more
This paper proposes a jailbreaking prompt detection method for large language models (LLMs) to defend against jailbreak attacks. Although recent LLMs...
Desen Sun, Jason Hon, Howe Wang +3 more
With the rapid advancement of generative AI, users increasingly rely on image-generation models for image design and creation. To achieve faithful...
Zheng Lin, Zhenxing Niu, Haoxuan Ji +1 more
This paper proposes a guaranteed defense method for large language models (LLMs) to safeguard against jailbreaking attacks. Drawing inspiration from...
Peiru Yang, Haoran Zheng, Tong Ju +6 more
Retrieval-augmented generation (RAG) is a widely adopted paradigm for enhancing LLMs in medical applications by incorporating expert multimodal...
Farzad Nourmohammadzadeh Motlagh, Mehrdad Hajizadeh, Mehryar Majd +3 more
Natural language interfaces to structured databases are becoming increasingly common, largely due to advances in large language models (LLMs) that...
Yue Li, Xiao Li, Hao Wu +5 more
Large Language Models (LLMs) are increasingly used for automated software development, making their ability to preserve secure coding practices...
Huilin Zhou, Jian Zhao, Yilu Zhong +7 more
Red teaming is critical for uncovering vulnerabilities in Large Language Models (LLMs). While automated methods have improved scalability, existing...
Yiyong Liu, Chia-Yi Hsu, Chun-Ying Huang +3 more
LLM-powered coding agents increasingly make software supply chain decisions. They generate imports, recommend packages, and write installation...
Zeyuan Chen, Yihan Ma, Xinyue Shen +2 more
Large language models (LLMs) show strong performance across many applications, but their ability to memorize and potentially reveal training data...
Huiyu Xu, Zhibo Wang, Wenhui Zhang +4 more
Modern LLM agents solve complex tasks by operating in iterative execution loops, where they repeatedly reason, act, and self-evaluate progress to...
Md Farhamdur Reza, Richeng Jin, Tianfu Wu +1 more
Intent-obfuscation-based jailbreak attacks on multimodal large language models (MLLMs) transform a harmful query into a concealed multimodal input to...
Wesley Hanwen Deng, Mingxi Yan, Sunnie S. Y. Kim +5 more
Recent developments in AI safety research have called for red-teaming methods that effectively surface potential risks posed by generative AI models,...
Zheng Fang, Xiaosen Wang, Shenyi Zhang +2 more
Jailbreak attacks on audio language models (ALMs) optimize audio perturbations to elicit unsafe generations, and they typically update the entire...
Zekun Fei, Zihao Wang, Weijie Liu +4 more
Mixture-of-Experts (MoE) architectures have emerged as a leading paradigm for scaling large language models through sparse, routing-based...
Raja Sekhar Rao Dheekonda, Will Pearce, Nick Landers
AI systems are entering critical domains like healthcare, finance, and defense, yet remain vulnerable to adversarial attacks. While AI red teaming is...
Shravya Kanchi, Xiaoyan Zang, Ying Zhang +2 more
Developers create modern software applications (Apps) on top of third-party libraries (Libs). When library vulnerabilities are reachable through...
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