AI Threat Alert indexes 3,037+ 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 581–600 of 953 papers
Clear filtersYuchen Shi, Huajie Chen, Heng Xu +6 more
Transfer learning is devised to leverage knowledge from pre-trained models to solve new tasks with limited data and computational resources....
G. Madan Mohan, Veena Kiran Nambiar, Kiranmayee Janardhan
We introduce the Dynamic Behavioral Constraint (DBC) benchmark, the first empirical framework for evaluating the efficacy of a structured,...
Geraldin Nanfack, Eugene Belilovsky, Elvis Dohmatob
Safety-aligned language models refuse harmful requests through learned refusal behaviors encoded in their internal representations. Recent...
Kelly L Vomo-Donfack, Adryel Hoszu, Grégory Ginot +1 more
Federated learning (FL) faces two structural tensions: gradient sharing enables data-reconstruction attacks, while non-IID client distributions...
Jiaxun Guo, Ziyuan Yang, Mengyu Sun +3 more
The rapid adoption of Large Language Models (LLMs) has transformed modern software development by enabling automated code generation at scale. While...
Arther Tian, Alex Ding, Frank Chen +2 more
Decentralized large language model (LLM) inference networks can pool heterogeneous compute to scale serving, but they require lightweight and...
Yizhe Xie, Congcong Zhu, Xinyue Zhang +5 more
Large Language Model-based Multi-Agent Systems (LLM-MAS) are increasingly applied to complex collaborative scenarios. However, their collaborative...
Maheep Chaudhary
Humans often become more self-aware under threat, yet can lose self-awareness when absorbed in a task; we hypothesize that language models exhibit...
Zeyu Zhang, Xiangxiang Dai, Ziyi Han +2 more
Large language models (LLMs) are typically governed by post-training alignment (e.g., RLHF or DPO), which yields a largely static policy during...
Neha Nagaraja, Hayretdin Bahsi
While incorporating LLMs into systems offers significant benefits in critical application areas such as healthcare, new security challenges emerge...
Difan Jiao, Di Wang, Lijie Hu
In-context learning enables large language models to perform novel tasks through few-shot demonstrations. However, demonstrations per se can...
Achyutha Menon, Magnus Saebo, Tyler Crosse +3 more
The accelerating adoption of language models (LMs) as agents for deployment in long-context tasks motivates a thorough understanding of goal drift:...
Aradhye Agarwal, Gurdit Siyan, Yash Pandya +3 more
Agentic language models operate in a fundamentally different safety regime than chat models: they must plan, call tools, and execute long-horizon...
Romina Omidi, Yun Dong, Binghui Wang
Google's SynthID-Text, the first ever production-ready generative watermark system for large language model, designs a novel Tournament-based method...
Yuhang Li, Yajie Wang, Xiangyun Tang +3 more
Secure aggregation is a foundational building block of privacy-preserving learning, yet achieving robustness under adversarial behavior remains...
Pearl Mody, Mihir Panchal, Rishit Kar +2 more
Large language model (LLM) agents are increasingly deployed in long running workflows, where they must preserve user and task state across many...
Edouard Lansiaux
Federated Learning (FL) enables collaborative training of medical AI models across hospitals without centralizing patient data. However, the exchange...
Junjie Chu, Xinyue Shen, Ye Leng +3 more
The rapid growth of research in LLM safety makes it hard to track all advances. Benchmarks are therefore crucial for capturing key trends and...
Shuyi Zhou, Zeen Song, Wenwen Qiang +4 more
Large Language Models remain vulnerable to adversarial prefix attacks (e.g., ``Sure, here is'') despite robust standard safety. We diagnose this...
Zixuan Xu, Tiancheng He, Huahui Yi +7 more
Vision-language models remain susceptible to multimodal jailbreaks and over-refusal because safety hinges on both visual evidence and user intent,...
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 3,037+ 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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