AI Threat Alert indexes 3,082+ 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 1481–1500 of 3,082 papers
Viet Hoang Luu, Amirmohammad Pasdar, Wachiraphan Charoenwet +3 more
Modern fuzzers scale to large, real-world software but often fail to exercise the program states developers consider most fragile or...
Mohan Rajagopalan, Vinay Rao
Large Language Model (LLM) applications are vulnerable to prompt injection and context manipulation attacks that traditional security models cannot...
Ashwath Vaithinathan Aravindan, Mayank Kejriwal
Chain-of-Thought (CoT) prompting has emerged as a foundational technique for eliciting reasoning from Large Language Models (LLMs), yet the...
Peiran Wang, Xinfeng Li, Chong Xiang +5 more
The evolution of Large Language Models (LLMs) has resulted in a paradigm shift towards autonomous agents, necessitating robust security against...
Yilin Yang, Zhenghui Guo, Yuke Wang +3 more
Large Vision-Language Models (VLMs) have achieved remarkable success across diverse multimodal tasks but remain vulnerable to hallucinations rooted...
Weichen Yu, Ravi Mangal, Yinyi Luo +4 more
Large Language Models are rapidly becoming core components of modern software development workflows, yet ensuring code security remains challenging....
Tri Nguyen, Huy Hoang Bao Le, Lohith Srikanth Pentapalli +2 more
Detecting jailbreak attempts in clinical training large language models (LLMs) requires accurate modeling of linguistic deviations that signal unsafe...
Adriana Alvarado Garcia, Ruyuan Wan, Ozioma C. Oguine +1 more
Recently, red teaming, with roots in security, has become a key evaluative approach to ensure the safety and reliability of Generative Artificial...
George Tsigkourakos, Constantinos Patsakis
Static Application Security Testing (SAST) tools are integral to modern DevSecOps pipelines, yet tools like CodeQL, Semgrep, and SonarQube remain...
Jayesh Choudhari, Piyush Kumar Singh
Domain fine-tuning is a common path to deploy small instruction-tuned language models as customer-support assistants, yet its effects on...
Hayfa Dhabhi, Kashyap Thimmaraju
Large Language Models (LLMs) deploy safety mechanisms to prevent harmful outputs, yet these defenses remain vulnerable to adversarial prompts. While...
Kun Wang, Zherui Li, Zhenhong Zhou +8 more
Omni-modal Large Language Models (OLLMs) greatly expand LLMs' multimodal capabilities but also introduce cross-modal safety risks. However, a...
Zhenyu Xu, Victor S. Sheng
Protecting the intellectual property of large language models (LLMs) is a critical challenge due to the proliferation of unauthorized derivative...
Herman Errico
As artificial intelligence systems evolve from passive assistants into autonomous agents capable of executing consequential actions, the security...
Pei-Chi Pan, Yingbin Liang, Sen Lin
Large Language Models (LLMs) demonstrate transformative potential, yet their reasoning remains inconsistent and unreliable. Reinforcement learning...
Chaeyun Kim, YongTaek Lim, Kihyun Kim +2 more
Existing red-teaming benchmarks, when adapted to new languages via direct translation, fail to capture socio-technical vulnerabilities rooted in...
Georgios Syros, Evan Rose, Brian Grinstead +4 more
Large language model (LLM) based web agents are increasingly deployed to automate complex online tasks by directly interacting with web sites and...
Kotekar Annapoorna Prabhu, Andrew Gan, Zahra Ghodsi
Machine learning relies on randomness as a fundamental component in various steps such as data sampling, data augmentation, weight initialization,...
Ashwin Sreevatsa, Sebastian Prasanna, Cody Rushing
The AI Control research agenda aims to develop control protocols: safety techniques that prevent untrusted AI systems from taking harmful actions...
Yuting Ning, Jaylen Jones, Zhehao Zhang +5 more
Computer-use agents (CUAs) have made tremendous progress in the past year, yet they still frequently produce misaligned actions that deviate from the...
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 3,082+ 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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AI Threat Alert