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 221–240 of 562 papers
Clear filtersJavad M Alizadeh, Genhui Zheng, Chiu C Tan +7 more
People experiencing homelessness (PEH) face substantial barriers to accessing timely, accurate information about community services. DreamKG...
Hanbo Huang, Xuan Gong, Yiran Zhang +2 more
Large language model (LLM) watermarking has emerged as a promising approach for detecting and attributing AI-generated text, yet its robustness to...
Jinhua Wang, Biswa Sengupta
Cross-language migration of large software systems is a persistent engineering challenge, particularly when the source codebase evolves rapidly. We...
Ricardo Bessa, Rui Claro, João Trindade +1 more
The application of Machine Learning techniques in code generation is now a common practice for most developers. Tools such as ChatGPT from OpenAI...
Dzenan Hamzic, Florian Skopik, Max Landauer +2 more
Cyber threat intelligence (CTI) analysts must answer complex questions over large collections of narrative security reports. Retrieval-augmented...
Xiaomeng Hu, Yinger Zhang, Fei Huang +7 more
AI agents are expected to perform professional work across hundreds of occupational domains (from emergency department triage to nuclear reactor...
Yuchen Chen, Yuan Xiao, Chunrong Fang +2 more
The proliferation of large language models for code (CodeLMs) and open-source contributions has heightened concerns over unauthorized use of source...
Wenbo Hu, Xin Chen, Yan Gao-Tian +3 more
Group Relative Policy Optimization (GRPO) has emerged as the de facto Reinforcement Learning (RL) objective driving recent advancements in Multimodal...
Runpeng Geng, Chenlong Yin, Yanting Wang +2 more
Prompt injection attacks pose serious security risks across a wide range of real-world applications. While receiving increasing attention, the...
Wenhao Yuan, Chenchen Lin, Jian Chen +3 more
In large language model (LLM) agents, reasoning trajectories are treated as reliable internal beliefs for guiding actions and updating memory....
Aryaman Arora, Zhengxuan Wu, Jacob Steinhardt +1 more
In language model interpretability research, \textbf{circuit tracing} aims to identify which internal features causally contributed to a particular...
Yu Liang, Liangxin Liu, Longzheng Wang +5 more
Generative reward models (GRMs) have emerged as a promising approach for aligning Large Language Models (LLMs) with human preferences by offering...
Hanyi Liu, Zhonghao Jiu, Minghao Wang +2 more
Implicit artistic influence, although visually plausible, is often undocumented and thus poses a historically constrained attribution problem:...
Yuanhang Li
Operating LEO mega-constellations requires translating high-level operator intents ("reroute financial traffic away from polar links under 80 ms")...
Phan The Duy, Nguyen Viet Duy, Khoa Ngo-Khanh +2 more
While recent approaches leverage large language models (LLMs) and multi-agent pipelines to automatically generate proof-of-concept (PoC) exploits...
Baoshun Tong, Haoran He, Ling Pan +2 more
Vision-Language-Action (VLA) models have achieved remarkable success in robotic manipulation. However, their robustness to linguistic nuances remains...
Geert Trooskens, Aaron Karlsberg, Anmol Sharma +6 more
We study compiled AI, a paradigm in which large language models generate executable code artifacts during a compilation phase, after which workflows...
Cheng Xu, Changhong Jin, Yingjie Niu +5 more
The rapid development of Large Language Models (LLMs) has transformed fake news detection and fact-checking tasks from simple classification to...
Houzhe Wang, Xiaojie Zhu, Chi Chen
With the increasing importance of data privacy and security, federated unlearning has emerged as a novel research field dedicated to ensuring that...
Kanishk Jain, Qian Yang, Shravan Nayak +3 more
Vision-language Models (VLMs), despite achieving strong performance on multimodal benchmarks, often misinterpret straightforward visual concepts that...
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