AI Threat Alert indexes 3,082+ 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 1321–1340 of 3,082 papers
Vladimer Khasia
The pursuit of world model based artificial intelligence has predominantly relied on projecting high-dimensional observations into parameterized...
Idan Habler, Vineeth Sai Narajala, Stav Koren +2 more
Retrieval-Augmented Generation (RAG) systems are essential to contemporary AI applications, allowing large language models to obtain external...
Bruce W. Lee, Chen Yueh-Han, Tomek Korbak
Frontier AI agents may pursue hidden goals while concealing their pursuit from oversight. Alignment training aims to prevent such behavior by...
Satyam Kumar Navneet, Joydeep Chandra, Yong Zhang
Large Language Models (LLMs) are increasingly used to ``professionalize'' workplace communication, often at the cost of linguistic identity. We...
Lan Zhang, Chengsi Liang, Zeming Zhuang +4 more
Semantic communication (SemCom) redefines wireless communication from reproducing symbols to transmitting task-relevant semantics. However, this...
Sarthak Munshi, Manish Bhatt, Vineeth Sai Narajala +4 more
While prior work has focused on projecting adversarial examples back onto the manifold of natural data to restore safety, we argue that a...
Kimberly T. Mai, Anna Gausen, Magda Dubois +5 more
AI is increasingly being used to assist fraud and cybercrime. However, it is unclear the extent to which current large language models can provide...
Zheng Gao, Xiaoyu Li, Zhicheng Bao +2 more
Generative images have proliferated on Web platforms in social media and online copyright distribution scenarios, and semantic watermarking has...
Yongchang Zhang, Oliver Ma, Tianyi Liu +2 more
Recent large vision-language models (LVLMs) have demonstrated impressive reasoning ability by generating long chain-of-thought (CoT) responses....
Inderjeet Singh, Vikas Pahuja, Aishvariya Priya Rathina Sabapathy +8 more
Current stateless defences for multimodal agentic RAG fail to detect adversarial strategies that distribute malicious semantics across retrieval,...
Yonathan Arbel, Peter Salib, Simon Goldstein
Very soon, millions of AI agents will proliferate across the economy, autonomously taking billions of actions. Inevitably, things will go wrong....
Nazanin Mohammadi Sepahvand, Eleni Triantafillou, Hugo Larochelle +3 more
Large language models (LLMs) trained on webscale data can produce toxic outputs, raising concerns for safe deployment. Prior defenses, based on...
Xuan Chen, Hao Liu, Tao Yuan +3 more
Traditional phishing website detection relies on static heuristics or reference lists, which lag behind rapidly evolving attacks. While recent...
Mengxuan Hu, Vivek V. Datla, Anoop Kumar +4 more
Recent advances in alignment techniques such as Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), and Direct...
Morteza Eskandarian, Mahdi Rabbani, Arun Kaniyamattam +6 more
The current generation of large language models produces sophisticated social-engineering content that bypasses standard text screening systems in...
Xinfeng Li, Shenyu Dai, Kelong Zheng +4 more
Large language model (LLM) agents are rapidly becoming trusted copilots in high-stakes domains like software development and healthcare. However,...
Mohammed Cherifi
Public EV charging infrastructure suffers from significant failure rates -- with field studies reporting up to 27.5% of DC fast chargers...
Guangnian Wan, Qi Li, Gongfan Fang +2 more
Multimodal Diffusion Language Models (MDLMs) have recently emerged as a competitive alternative to their autoregressive counterparts. Yet their...
Yanna Jiang, Delong Li, Haiyu Deng +4 more
Agentic systems increasingly rely on reusable procedural capabilities, \textit{a.k.a., agentic skills}, to execute long-horizon workflows reliably....
Piyush Jaiswal, Aaditya Pratap, Shreyansh Saraswati +2 more
Large Language Models (LLMs) are widely deployed in real-world systems. Given their broader applicability, prompt engineering has become an efficient...
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 3,082+ 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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