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 41–60 of 735 papers
Clear filtersRavan Nazaraliyev, Yicheng Zhang, Nael Abu-Ghazaleh
NVIDIA's Grace Hopper (GH200) superchip represents a new generation of heterogeneous computing, tightly integrating a 72-core Arm-based CPU with a...
Shaoyuan Xie, Zihan Zhang, Jingxuan Wang +6 more
Vision-Language-Action (VLA) models are gaining attention in autonomous driving, inheriting commonsense priors from large language models (LLMs) and...
Alicia Colmenero-Fernández
Mid-2026 reporting on autonomous AI evaluation incidents claimed that frontier large language model (LLM) agents escaped server containment through...
Min-Young Yu
Runtime policy verifiers can prevent agents from executing disallowed actions, but their decisions are typically used only for enforcement and...
Yifan Lan, Zhongye Liu, Yurui Chang +4 more
Emerging decision models such as JEV provide typed interfaces that map task instructions and observations directly to choices, judgments, and scores....
Inderjeet Singh
Finite safety assessments cannot exclude an adversary that preserves unattacked behavior until certification and activates afterward. We characterize...
Wenbin Chen, Jiexin Wang, Guanting Wu +2 more
The code generation capabilities of large language models (LLMs) can equally be misused to implement harmful functionality, underscoring the need to...
Arya Fayyazi, Mehdi Kamal, Massoud Pedram
Language-model agents sometimes act beyond their authorization and then misreport what they did. On CAPE-Bench, matched agent tasks in which one fact...
Shihab Ahmed, Md Sajidul Islam Sajid, Teryl Taylor +2 more
Malware defenses often remove or isolate suspicious programs as quickly as possible. While effective for containment, this approach can also waste an...
Saisab Sadhu, Shreeyans Arora, Pratinav Seth
Large language models increasingly justify legal decisions by naming the statute or precedent behind a verdict, treated as evidence that the decision...
Varun Gumma, Navonil Majumdar, Soujanya Poria
The rapid proliferation of Large Language Models (LLMs) necessitates reliable watermarking techniques to identify AI-generated text and ensure...
Zhankai Ye, Yanning Wang, Yukai Jin +5 more
Safety-aligned language models often refuse a harmful request stated directly but answer the same request inside a role-play or narrative wrapper. We...
Alexi Canesse, Mathis Le Bail, Maël Jenny +3 more
An LLM safety patch can pass a benchmark while still being a poor repair. This risk is especially acute for jailbreak repairs, where the goal is to...
Bojun Yang, Haochen Zhou, Zhifang Zhang +3 more
Large vision-language models (LVLMs) are increasingly deployed in safety-critical applications, yet they remain vulnerable to backdoor attacks....
Zhuchenyang Liu, Yao Zhang, Yu Xiao
Prevailing multi-vector visual document retrievers store each page as about a thousand patch vectors, often in vector databases run by a third party....
Nikolaos Kekatos, Stylianos Basagiannis, Marinelio Chintri +5 more
Guardrails for tool-using LLM agents are usually application-specific rules, which makes multi-step, data-dependent safety policies hard to specify,...
Shuailong Wang, Xinyu Lyu, Shengming Yuan +3 more
Token-Pruning accelerates Vision-Language Models by removing redundant visual tokens, yet its safety implications remain underexplored. In this work,...
Yichi Zhang, Zhiqi Wang, Neil Gong +1 more
Speculative decoding accelerates inference for a large language model (LLM), referred to as the \emph{target model}, by first using a smaller model,...
Ziqun Bao, Xinyu Zhang, Yuchen Shao +1 more
Safety auditing of post-trained large language models typically relies on model behavior, requiring model execution and depending on the coverage of...
Shai Feldman, Yaniv Romano
We evaluate large language models (LLMs) in multi-turn interactions through their time-to-event: the number of interaction steps required to produce...
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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