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 521–540 of 562 papers
Clear filtersKeyang Zhang, Zeyu Chen, Xuan Feng +4 more
The security of scripting languages such as PowerShell is critical given their powerful automation and administration capabilities, often exercised...
Hoang-Chau Luong, Lingwei Chen
Low-Rank Adaptation (LoRA) is widely used for parameter-efficient fine-tuning of large language models, but it is notably ineffective at removing...
Tianshi Li
On December 4, 2025, Anthropic released Anthropic Interviewer, an AI tool for running qualitative interviews at scale, along with a public dataset of...
Zhi Yang, Runguo Li, Qiqi Qiang +15 more
Financial agents powered by large language models (LLMs) are increasingly deployed for investment analysis, risk assessment, and automated...
Suyash Mishra, Qiang Li, Srikanth Patil +1 more
Vision Language Models (VLMs) are poised to revolutionize the digital transformation of pharmacyceutical industry by enabling intelligent, scalable,...
Konstantinos E. Kampourakis, Vyron Kampourakis, Efstratios Chatzoglou +2 more
Realistic, large-scale, and well-labeled cybersecurity datasets are essential for training and evaluating Intrusion Detection Systems (IDS). However,...
Seyeon Jeong, Yeonjun Choi, JongWook Kim +1 more
Large Language Models (LLMs) suffer from hallucinations and factual inaccuracies, especially in complex reasoning and fact verification tasks....
Huawei Zheng, Xinqi Jiang, Sen Yang +3 more
Large language models (LLMs) are increasingly applied in specialized domains such as finance and healthcare, where they introduce unique safety...
Jacob Ede Levine, Yun Lyan Luo, Sai Chandra Kosaraju
The design of reliable, valid, and diverse molecules is fundamental to modern drug discovery, as improved molecular generation supports efficient...
Atharv Naphade
Retrieval-Augmented Generation (RAG) is the prevailing paradigm for grounding Large Language Models (LLMs), yet the mechanisms governing how models...
Xinyue Lou, Jinan Xu, Jingyi Yin +8 more
As Multimodal Large Language Models (MLLMs) become an indispensable assistant in human life, the unsafe content generated by MLLMs poses a danger to...
Haeun Jang, Hwan Chang, Hwanhee Lee
The deployment of Large Vision-Language Models (LVLMs) for real-world document question answering is often constrained by dynamic, user-defined...
Xiaoyu Xu, Minxin Du, Zitong Li +6 more
Although machine unlearning is essential for removing private, harmful, or copyrighted content from LLMs, current benchmarks often fail to faithfully...
Dinesh Srivasthav P, Ashok Urlana, Rahul Mishra +2 more
Machine unlearning aims to selectively remove the influence of specific training samples to satisfy privacy regulations such as the GDPR's 'Right to...
Jin Wang, Liang Lin, Kaiwen Luo +8 more
While Audio Large Language Models (ALLMs) have achieved remarkable progress in understanding and generation, their potential privacy implications...
Quy-Anh Dang, Chris Ngo, Truong-Son Hy
As large language models (LLMs) become integral to safety-critical applications, ensuring their robustness against adversarial prompts is paramount....
Zejian Chen, Chaozhuo Li, Chao Li +3 more
This paper provides a systematic survey of jailbreak attacks and defenses on Large Language Models (LLMs) and Vision-Language Models (VLMs),...
Shidong Cao, Hongzhan Lin, Yuxuan Gu +2 more
Chain-of-Thought (CoT) reasoning improves multi-step mathematical problem solving in large language models but remains vulnerable to exposure bias...
Xiangzhe Yuan, Zhenhao Zhang, Haoming Tang +1 more
As LLMs gain persuasive agentic capabilities through extended dialogues, they introduce novel risks in multi-turn conversational scams that...
Nick Pepper, Adam Keane, Amy Hodgkin +11 more
This paper presents the first probabilistic Digital Twin of operational en route airspace, developed for the London Area Control Centre. The Digital...
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.
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