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 761–780 of 866 papers
Clear filtersMohammad Abdul Rehman, Syed Imad Ali Shah, Abbas Anwar +2 more
The remarkable capabilities of Large Language Models (LLMs) in natural language understanding and generation have sparked interest in their potential...
Yao Huang, Yitong Sun, Yichi Zhang +3 more
Despite the remarkable advances of Large Language Models (LLMs) across diverse cognitive tasks, the rapid enhancement of these capabilities also...
Luca Belli, Kate Bentley, Will Alexander +5 more
We introduce VERA-MH (Validation of Ethical and Responsible AI in Mental Health), an automated evaluation of the safety of AI chatbots used in mental...
Bin Liu, Yanjie Zhao, Guoai Xu +1 more
Large language model (LLM) agents have demonstrated remarkable capabilities in software engineering and cybersecurity tasks, including code...
Trilok Padhi, Pinxian Lu, Abdulkadir Erol +5 more
Large Language Model (LLM) agents are powering a growing share of interactive web applications, yet remain vulnerable to misuse and harm. Prior...
Matan Levi, Daniel Ohayon, Ariel Blobstein +3 more
Large language models (LLMs) are transforming everyday applications, yet deployment in cybersecurity lags due to a lack of high-quality,...
Qiushi Wu, Yue Xiao, Dhilung Kirat +3 more
Fixing bugs in large programs is a challenging task that demands substantial time and effort. Once a bug is found, it is reported to the project...
Yibo Peng, James Song, Lei Li +6 more
Code agents are increasingly trusted to autonomously fix bugs on platforms such as GitHub, yet their security evaluation focuses almost exclusively...
Xiuyuan Chen, Tao Sun, Dexin Su +38 more
Current benchmarks for AI clinician systems, often based on multiple-choice exams or manual rubrics, fail to capture the depth, robustness, and...
Jonghyun Park, Minhyuk Seo, Jonghyun Choi
One of the key challenges of modern AI models is ensuring that they provide helpful responses to benign queries while refusing malicious ones. But...
Ivan Dubrovsky, Anastasia Orlova, Illarion Iov +3 more
Benchmarking outcomes increasingly govern trust, selection, and deployment of LLMs, yet these evaluations remain vulnerable to semantically...
Xin Zhao, Xiaojun Chen, Bingshan Liu +3 more
Large language models (LLMs) with Mixture-of-Experts (MoE) architectures achieve impressive performance and efficiency by dynamically routing inputs...
Yuan Feng, Haoyu Guo, JunLin Lv +2 more
Large language models have revolutionized natural language processing, yet their deployment remains hampered by the substantial memory and runtime...
Juan Ren, Mark Dras, Usman Naseem
Large Vision-Language Models (LVLMs) unlock powerful multimodal reasoning but also expand the attack surface, particularly through adversarial inputs...
Ruoyu Sun, Da Song, Jiayang Song +2 more
As Large Language Models (LLMs) continue to revolutionize Natural Language Processing (NLP) applications, critical concerns about their...
João A. Leite, Arnav Arora, Silvia Gargova +5 more
Large Language Models (LLMs) can generate human-like disinformation, yet their ability to personalise such content across languages and demographics...
Blazej Manczak, Eric Lin, Francisco Eiras +2 more
Large language models (LLMs) are rapidly transitioning into medical clinical use, yet their reliability under realistic, multi-turn interactions...
Dongsen Zhang, Zekun Li, Xu Luo +3 more
The Model Context Protocol (MCP) standardizes how large language model (LLM) agents discover, describe, and call external tools. While MCP unlocks...
Lipeng He, Vasisht Duddu, N. Asokan
Chatbot providers (e.g., OpenAI) rely on tiered subscription schemes to generate revenue, offering basic models for free users, and advanced models...
Shuo Chen, Zonggen Li, Zhen Han +7 more
Deep Research (DR) agents built on Large Language Models (LLMs) can perform complex, multi-step research by decomposing tasks, retrieving online...
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