AI Threat Alert indexes 3,771+ 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 341–360 of 3,771 papers
Bowen Sun, Zhengyue Zhao, Xiaogeng Liu +2 more
Most large language model services use stateless defenses, which judge only the current request, to refuse harmful tasks. Decomposition attacks...
Zhida He, Xiaoyu Wen, Han Qi +5 more
Reliable jailbreak evaluation is essential for assessing LLM safety, but most existing studies rely solely on attack success rate (ASR) without...
Rabimba Karanjai, Yang Lu, Nour Diallo +4 more
AI agents increasingly act rather than merely read: across the Model Context Protocol (MCP) ecosystem, the share of deployed tools that modify...
Md Abdullahil Oaphy, Anhao Xiang, Zongxing Xie +3 more
Multimodal large language models (MLLMs) are increasingly used to interact with screenshots, scanned documents, diagrams, and other visually grounded...
Rabimba Karanjai, Yang Lu, Richard Williamson +5 more
Autonomous AI agents are emerging as interfaces for decentralized finance (DeFi) actions such as swaps, lending operations, and yield management....
Mark Russinovich
Safety alignment in open-weight language models is trivially removable: abliteration projects a refusal-mediating direction out of the weights in...
Mehrdad Ghassabi
Retrieval-Augmented Generation (RAG) has significantly enhanced the performance of large language models (LLMs), yet these systems remain vulnerable...
Sibo Liu
A personal language agent learns a fact from one audience and may later place it in the prompt it assembles for another. This memory-to-context step...
Abyad Enan, Sagar Dasgupta, Mizanur Rahman +1 more
Autonomous vehicles (AVs) depend on reliable Global Navigation Satellite System (GNSS) positioning. However, spoofed GNSS signals can induce...
Tong Zhang, Motasem Alfarra, Carlos Hinojosa +2 more
As text-to-image generative models advance, they raise critical safety concerns, particularly the generation of Not-Safe-For-Work (NSFW) content such...
Jiawei Liu, Jiacheng Guo, Tian Zhang +4 more
Foundation models are increasingly used for perception, reasoning, planning, and action generation in embodied agents, creating security risks that...
Chang Liu, Yuni Lai, Mingyue Cui +5 more
Multimodal Retrieval Augmented Generation (M-RAG) is increasingly vulnerable to adversarial attacks where malicious data are crafted to produce...
Abdullah Alghamdi, Siamak Layeghy, Marius Portmann
We propose a two-stage large language model (LLM) framework for zero-shot detection of insider threats and advanced persistent threats (APTs) from...
Stylianos Kampakis, Fabio Rovai, Marcos Charalambides +2 more
Cyber threats continue to escalate in both frequency and sophistication, necessitating more adaptive and scalable defense strategies. This paper...
Zonghao Ying, Xiangfan Wu, Huiyu Wu +4 more
We assess indirect prompt injection in DeepSeek Harness (DSH), using AI-Infra-Guard (A.I.G) to construct tests, deliver controlled taint, execute...
Zonghao Ying, Xiangfan Wu, Huiyu Wu +4 more
We assess indirect prompt injection in DeepSeek Harness (DSH), using AI-Infra-Guard (A.I.G) to construct tests, deliver controlled taint, execute...
Mingxiao Liu, Zhoumian Jiang, Jianan Ma +4 more
Autonomous AI agents tackling Long Horizon Tasks depend on marketplace skills that are certified one at a time: a scanner returns a safety verdict...
Jun He, Deying Yu
Operational telemetry is predominantly engineered for human reading: systems repeatedly serialize verbose prose, static keys, and redundant context...
Konstantinos E. Kampourakis, Vasileios Gkioulos, Sokratis Katsikas
Digital Twins (DTs) are increasingly used to monitor and analyze Cyber Physical Systems (CPS). However, in adversarial environments, the fidelity of...
Md Habibur Rahman, Jaeho Kim
LLM agents are stateless and rely on external memory to carry context between steps. Because agents treat that memory as trustworthy, an adversary...
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 3,771+ 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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