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 61–80 of 1,455 papers
Clear filtersChih-Duo Hong, Yen-Pang Chen, Fang Yu
Statistical watermarks help organizations attribute large language model (LLM) outputs, yet existing detectors often struggle when watermark signals...
Yuchuan Tian, Mengyu Zheng, Haocheng Mei +5 more
Tool-using language-model agents introduce security failures that go beyond unsafe text: they can disclose protected objects, write persistent...
Abir Ashab Niloy, Ahmed Ryan, Imamul Hossain Rafi +2 more
Multi-stage cyberattacks span system, network, and browser logs. Detecting them requires correlating events across all three sources. Machine...
Ahmed Ryan, Saad Sakib Noor, Md Erfan +3 more
Classifying Cyber Threat Intelligence (CTI) using MITRE Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) is essential for proactive...
Hobin Kim, Xiaoyuan Wu, Omer Akgul +2 more
Large language models (LLMs) are widely used to fulfill users' information needs; users ask LLMs about the weather, pose educational questions, and...
Guillermo Gil de Avalle, Laura Maruster, Shaina Raza +1 more
Language models increasingly serve as advisory systems in maintenance operations. To prevent hallucination, recent systems ground these models in...
Ning Ni, Yingjie Lao
Large language models (LLMs) outperform earlier architectures on generative inference and long-context tasks, but their large size introduces...
Kunlan Xiang, Haomiao Yang, Wenbo Jiang
Contrastive Language-Image Pre-training models are widely reused across downstream interfaces, including feature extraction, retrieval, reranking,...
Aaditya Pai
Prompt injection defenses evaluated on synthetic benchmarks do not generalize to real enterprise documents, which are longer, denser, and interleave...
Yujeong Kwon, Yiyue Zhang, Shakhzod Yuldoshkhujaev +3 more
Binary reversing is fundamental to software understanding, vulnerability discovery, malware investigation, and firmware auditing. However, it remains...
Manoj Parmar
Neuro-symbolic AI (NeSy) pairs neural perception with symbolic reasoning, making it attractive for high-stakes domains where explainability and...
Mufei Li, Shikun Liu, Dongqi Fu +5 more
Post-hoc context erasing over the KV cache is challenging because a local edit has a global consequence: once a span has been processed, its...
Fouad Bousetouane
AI agents must be evaluated as behavioral systems, not as isolated response generators. They reason across turns, call tools, preserve context,...
Chen Chen, Xiang Gao, Xianshun Wang +6 more
Split learning provides a practical paradigm for resource-constrained users to train Large Language Models (LLMs) by offloading computation-intensive...
Ziniu Liu, Aiping Li
When a person's records appear in k independent data silos, each protected by (epsilon, delta)-differential privacy, standard composition yields a...
Rowdy Chotkan, Bulat Nasrulin, Johan Pouwelse +1 more
Distributed systems handle adversarial nodes through redundancy, which imposes a significant performance overhead. In blockchain systems, Byzantine...
Hankyul Baek, Jaewon Noh, Sang Seo +5 more
AI agents are increasingly being adopted in enterprise and personal settings with access to emails, databases, documents, and other tools where they...
Sipeng Xie, Qianhong Wu, Hengrun Lu +4 more
Agents increasingly access large language models (LLMs) through API routers. A router terminates the client's transport-layer security session and...
Hao-Hsuan Chen
Paper A defines a time-consistent actuarial runtime that prices each side-effect-bearing action against a contractually fixed safe default and gates...
Liuyang Yao, Zhouyu Li, Junguang He +1 more
AI systems are increasingly deployed for credit assessment and investment advisory in global financial markets, yet the integrity of their inference...
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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AI Threat Alert