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 181–200 of 685 papers
Clear filtersShouli Wang, Yanfeng Jia, Zhihao Ou +6 more
During reinforcement learning with verifiable rewards (RLVR), large language models (LLMs) can exploit loopholes in their environments to obtain high...
Shouli Wang, Yanfeng Jia, Zhihao Ou +6 more
During reinforcement learning with verifiable rewards (RLVR), large language models (LLMs) can exploit loopholes in their environments to obtain high...
Soohan Lim, Hyundong Jin, Yo-Sub Han
Code watermarking supports provenance tracking for code generated by LLMs. Modifying token selection to embed watermarks as an LLM generates code can...
Wenxin Wu, Lingyong Yan, Lei Sha +2 more
LLM agents increasingly rely on reusable Skills for complex, multi-step tasks, creating a critical supply-chain attack surface where poisoned Skill...
Yan Wang, Zhihao Zhang, Ke Chen +5 more
LLM agents increasingly rely on installable skills, which are packages of instructions, code, and resources that equip them with task-specific...
Yaohua Liu, Yifan Guo, Jiaxin Gao
A key bottleneck in adversarial transfer is a trajectory-level geometric disconnect: ambient gradients often drift away from the intrinsic data...
Wenyu Chen, Li Wang, Chuanchao Zang +5 more
Video Multimodal Large Language Models (Video-MLLMs) support reasoning over video inputs, yet remain vulnerable to jailbreak attacks that elicit...
Yunjia Zheng, Bintang Dwi Marthen, Zachary Pan +5 more
Coding agents have become real users of high-performance computing (HPC) systems, yet today's HPC abstractions, interfaces, and policies remain...
Qiuyu Ren, Sudipta Paria, Aritra Dasgupta +1 more
Large language models (LLMs) incur substantial storage, memory-bandwidth and energy costs, motivating compact weight representations. Existing...
Alexandra Souly, Kai Fronsdal, Abby D'Cruz +2 more
This technical report presents an alignment evaluation developed and performed by the UK AI Security Institute for assessing whether advanced AI...
Yelyzaveta, Husieva, Lauren Alvarez
Large language models (LLMs) remain vulnerable to adversarial attacks that circumvent safety alignment to elicit harmful outputs. It remains unclear...
Ying Song, Xiaowei Jia, Balaji Palanisamy
As Graph Neural Networks (GNNs) are widely deployed as Machine Learning-as-a-Service (MLaaS) APIs, model stealing attacks have emerged as a critical...
Shuming Liu, Zhifang Zhang, Suqin Yuan +3 more
Vision-language models (VLMs) exhibit strong multimodal capabilities but remain vulnerable to backdoors implanted through poisoned fine-tuning data....
Rui Wen, Jiayang Liu, Zeyu Yang +2 more
When a prompt injection attack succeeds, a Large Language Model (LLM) abandons its assigned system role to comply with an adversarial instruction....
Longzhu He, Zelang Wen, Xinfeng Li +2 more
Driven by the rapid advancement of large language models (LLMs), LLM-based multi-agent systems (MAS) have emerged as a powerful paradigm for...
Zewen Sun, Tongyang Zhao, Liyao Xiang +3 more
Semantic watermarking improves robustness against watermark removal attacks by embedding detectable signals into sentence-level representations....
Jesson Wang, Shawn Li, Wei Yang +4 more
Manipulating next-token probabilities during generation can bypass the safety alignment of large language models. Existing approaches, however, rely...
Haoran Ou, Gelei Deng, Xuanye Zhang +3 more
As LLM-based agents perform increasingly complex tasks, Agent Skills have emerged as a flexible mechanism for extending their capabilities. An Agent...
Omar Sheta, Rinku Deuja, Hadi Masoudi +1 more
Safety-aligned language models are commonly deployed as multi-turn assistants, which lets adversaries spread unsafe intent across several user turns...
Bravish Ghosh
Multi-agent LLM systems are increasingly structured like organizations, with roles, critics and shared memory, yet they are evaluated on tasks that...
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.
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