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 481–500 of 4,319 papers
Fahao Chen, Linkang Du, Jinhao Zhou +2 more
Sparse attention is widely used to accelerate long-context inference in modern large language models (LLMs), but its input-dependent execution...
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...
Ozgur Can Seckin, Shalmoli Ghosh, Alessandro Flammini +3 more
Large language models (LLMs) can influence people's beliefs, yet little is known about whether and how they can manipulate each other. To investigate...
Zhuo Liu, Moxin Li, Zhixin Ma +3 more
Large language model (LLM) agents are vulnerable to safety risks such as injected malicious instructions or misleading information, motivating...
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...
Haotian Deng, Wenbin Xing, Gang Xu +5 more
Vision-Language Models (VLMs) remain vulnerable to cross-modal implicit risks: visual and textual inputs that appear benign in isolation can jointly...
Jia Cai
Electronic invoices are replacing paper invoices worldwide, but today's centralized architectures leave three problems unsolved on the consumption...
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....
Ahmed Nader Ahmed, Omar Moured, Mughni Irfan Mohammed Abdul +2 more
Long-horizon robotic tasks are vulnerable to unexpected environmental changes that can render planned actions ineffective or unsafe. To address this,...
Sabrina Kaniewski, Tim Krämer, Julius Bächle +3 more
Retrieval-Augmented Generation (RAG) is increasingly used to enhance Large Language Model (LLM)-based software vulnerability detection by grounding...
Yurong Hao, Wen Zhou, Guowei Guan +3 more
Advancing beyond traditional static scoring models, LLM-powered agentic recommender systems (LLM-ARS) instantiate users and items as autonomous...
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...
Sayan Biswas, Jade Garcia Bourrée, Rachid Guerraoui +5 more
Decentralized large language model (LLM) fine-tuning lets organizations collaboratively train a shared LLM on data they cannot pool, without a...
Zewen Sun, Tongyang Zhao, Liyao Xiang +3 more
Semantic watermarking improves robustness against watermark removal attacks by embedding detectable signals into sentence-level representations....
Yanjie Li, Xiangyu He, Xuelong Dai +1 more
Tool-using LLM agents remain vulnerable to indirect prompt injection because trusted instructions and untrusted observations share one context,...
Zhuo Chen, Hao Zeng, Jiawei Liu +6 more
The rapid growth of submissions and reviewing workload has accelerated the use of large language models (LLMs) in peer review. Prior studies suggest...
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.
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