AI Threat Alert indexes 3,055+ 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 881–900 of 955 papers
Clear filtersYi Liu, Weizhe Wang, Ruitao Feng +5 more
The rise of AI agent frameworks has introduced agent skills, modular packages containing instructions and executable code that dynamically extend...
Luoming Hu, Jingjie Zeng, Liang Yang +1 more
Enhancing the moral alignment of Large Language Models (LLMs) is a critical challenge in AI safety. Current alignment techniques often act as...
Yutao Mou, Zhangchi Xue, Lijun Li +4 more
While LLM-based agents can interact with environments via invoking external tools, their expanded capabilities also amplify security risks....
Jiawen Zhang, Yangfan Hu, Kejia Chen +7 more
Fine-tuning is an essential and pervasive functionality for applying large language models (LLMs) to downstream tasks. However, it has the potential...
Mohoshin Ara Tahera, Karamveer Singh Sidhu, Shuvalaxmi Dass +1 more
Large Language Models (LLMs) are increasingly adopted in healthcare to support clinical decision-making, summarize electronic health records (EHRs),...
Hanna Foerster, Tom Blanchard, Kristina Nikolić +6 more
AI agents are vulnerable to prompt injection attacks, where malicious content hijacks agent behavior to steal credentials or cause financial loss....
Greta Dolcetti, Giulio Zizzo, Sergio Maffeis
We present an experimental evaluation that assesses the robustness of four open source LLMs claiming function-calling capabilities against three...
Feng Zhang, Shijia Li, Chunmao Zhang +7 more
User simulators serve as the critical interactive environment for agent post-training, and an ideal user simulator generalizes across domains and...
Caitlin A. Stamatis, Jonah Meyerhoff, Richard Zhang +3 more
Large language models (LLMs) are increasingly used for mental health support, yet existing safety evaluations rely primarily on small,...
Renyang Liu, Kangjie Chen, Han Qiu +4 more
Image generation models (IGMs), while capable of producing impressive and creative content, often memorize a wide range of undesirable concepts from...
Ziqi Ding, Yunfeng Wan, Wei Song +7 more
CAPTCHAs are widely used by websites to block bots and spam by presenting challenges that are easy for humans but difficult for automated programs to...
Seong-Gyu Park, Sohee Park, Jisu Lee +2 more
Recent LLMs increasingly integrate reasoning mechanisms like Chain-of-Thought (CoT). However, this explicit reasoning exposes a new attack surface...
Erin Feiglin, Nir Hutnik, Raz Lapid
We investigate a failure mode of large language models (LLMs) in which plain-text prompts elicit excessive outputs, a phenomenon we term Overflow....
Abdelaziz Bounhar, Rania Hossam Elmohamady Elbadry, Hadi Abdine +3 more
Steering Large Language Models (LLMs) through activation interventions has emerged as a lightweight alternative to fine-tuning for alignment and...
Zhenhua Xu, Yiran Zhao, Mengting Zhong +4 more
The rapid growth of large language models raises pressing concerns about intellectual property protection under black-box deployment. Existing...
Dongryeol Lee, Yerin Hwang, Taegwan Kang +3 more
While large language models (LLMs) are increasingly used as automatic judges for question answering (QA) and other reference-conditioned evaluation...
Ruiqi Li, Zhiqiang Wang, Yunhao Yao +1 more
To standardize interactions between LLM-based agents and their environments, the Model Context Protocol (MCP) was proposed and has since been widely...
Weipeng Jiang, Xiaoyu Zhang, Juan Zhai +3 more
Emoticons are widely used in digital communication to convey affective intent, yet their safety implications for Large Language Models (LLMs) remain...
Mingxiang Tao, Yu Tian, Wenxuan Tu +3 more
Federated learning (FL) addresses data privacy and silo issues in large language models (LLMs). Most prior work focuses on improving the training...
Yixiao Peng, Hao Hu, Feiyang Li +5 more
While virtualization and resource pooling empower cloud networks with structural flexibility and elastic scalability, they inevitably expand 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 3,055+ 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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