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 81–96 of 96 papers
Clear filtersShuze Liu, Qianwen Guo, Yushun Dong
Large language models (LLMs) are increasingly deployed through hosted APIs, making model extraction a practical threat to model ownership and service...
Seungwon Jeong, Jiwoo Jeong, Hyeonjin Kim +2 more
As large language models (LLMs) are widely deployed, identifying their vulnerability through jailbreak attacks becomes increasingly critical....
Paulo Ricardo Ferreira Neves, Edson Rodrigues da Cruz Filho, Paulo Henrique Eleuterio Falsetti +7 more
Large Language Models (LLMs) have transformed natural language processing, but they remain vulnerable to Prompt Injection (PI) and Jailbreak (JB)...
Quanyan Zhu
Agentic artificial intelligence (AI) systems are transforming the risk landscape by extending beyond information generation to autonomous planning,...
Alexandre Cristovão Maiorano
Security teams routinely simulate attacks against their own systems to check whether their monitoring would catch a real intruder. These...
Jack Sanderson, Yihan Wang, Xiaoqian Lu +2 more
LLM post-training proceeds through multiple stages, e.g., supervised fine-tuning (SFT) followed by reinforcement learning from human feedback (RLHF)...
Manar Abouzaid, Yang Wang, Chenghua Lin +1 more
Large Language Models (LLMs) are vulnerable both to hallucination and adversarial manipulation. Although these problems are closely related, existing...
Yafan Huang, Sheng Di, Guanpeng Li
Large language models (LLMs) are increasingly integrated into high-performance computing (HPC) workflows, accelerating scientific discovery through...
Ran Liu, Min Yu, Mingqi Liu +7 more
In dynamic environments, large language models need to keep adapting to new tasks, but continual learning often suffers from forgetting, limited...
Choongwon Kang, Seungjong Sun, Hyunmin Jun +1 more
As multimodal large language models (MLLMs) have advanced to process video inputs, concerns have emerged about their potential for malicious misuse....
Yani Wang, Yilong Yang, Yang Liu +3 more
Multimodal Large Language Models (MLLMs) have recently demonstrated remarkable capabilities in content synthesis and autonomous reasoning. Previous...
Rakshit Naidu
Machine learning models trained on sensitive data can inadvertently leak population-level information about their training distributions -- a threat...
Yoshinari Fujinuma, Varun Gangal, Traian Rebedea +4 more
Large language model (LLM) agents increasingly rely on reusable skills i.e. documents describing task-specific procedures. However, this introduces a...
Boxuan Wang, Zhuoyun Li, Xiaowei Huang +1 more
Large language models (LLMs) excel in reasoning and knowledge-intensive tasks but remain vulnerable to prompt-level adversarial attacks that preserve...
Yuyang Gong, Miaokun Chen, Jiawei Liu +5 more
Retrieval-Augmented Generation (RAG) systems are widely deployed and increasingly influential, but their reliance on external corpora exposes new...
Seonwoo Kim, Jinwoo Kim, Daegyu Kang +2 more
Cyber threat intelligence (CTI) reports now serve as essential resources for capturing adversary tactics, techniques, and procedures observed in...
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