AI Threat Alert indexes 4,319+ 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 1641–1660 of 2,152 papers
Clear filtersPankayaraj Pathmanathan, Michael-Andrei Panaitescu-Liess, Cho-Yu Jason Chiang +1 more
Retrieval-Augmented Generation (RAG) has emerged as a promising paradigm to enhance large language models (LLMs) with external knowledge, reducing...
Giuseppe Canale, Kashyap Thimmaraju
Large Language Models (LLMs) are rapidly transitioning from conversational assistants to autonomous agents embedded in critical organizational...
Ruixuan Huang, Qingyue Wang, Hantao Huang +4 more
Mixture-of-Experts architectures have become the standard for scaling large language models due to their superior parameter efficiency. To...
Samaresh Kumar Singh, Joyjit Roy, Martin So
Recent attacks on critical infrastructure, including the 2021 Oldsmar water treatment breach and 2023 Danish energy sector compromises, highlight...
Heba Osama, Omar Elebiary, Youssef Qassim +4 more
Web applications increasingly face evasive and polymorphic attack payloads, yet traditional web application firewalls (WAFs) based on static rule...
Armstrong Foundjem, Lionel Nganyewou Tidjon, Leuson Da Silva +1 more
Machine learning (ML) underpins foundation models in finance, healthcare, and critical infrastructure, making them targets for data poisoning, model...
Karolina Korgul, Yushi Yang, Arkadiusz Drohomirecki +7 more
Web-based agents powered by large language models are increasingly used for tasks such as email management or professional networking. Their reliance...
Tsogt-Ochir Enkhbayar
Warning-framed content in training data (e.g., "DO NOT USE - this code is vulnerable") does not, it turns out, teach language models to avoid the...
Tian Li, Bo Lin, Shangwen Wang +1 more
Retrieval-Augmented Code Generation (RACG) is increasingly adopted to enhance Large Language Models for software development, yet its security...
Haoyang Li, Mingjin Li, Jinxin Zuo +5 more
LLM-based code agents(e.g., ChatGPT Codex) are increasingly deployed as detector for code review and security auditing tasks. Although CoT-enhanced...
Yifan Huang, Xiaojun Jia, Wenbo Guo +4 more
Large language models (LLMs) have revolutionized software development through AI-assisted coding tools, enabling developers with limited programming...
Ahmed M. Hussain, Salahuddin Salahuddin, Panos Papadimitratos
Current Large Language Models (LLMs) safety approaches focus on explicitly harmful content while overlooking a critical vulnerability: the inability...
Jiashuo Liu, Jiayun Wu, Chunjie Wu +5 more
The rapid proliferation of Large Language Models (LLMs) and diverse specialized benchmarks necessitates a shift from fragmented, task-specific...
Yifan Yao, Baojuan Wang, Jinhao Duan +4 more
Chat-based cybercrime has emerged as a pervasive threat, with attackers leveraging real-time messaging platforms to conduct scams that rely on...
Anselm Paulus, Ilia Kulikov, Brandon Amos +4 more
Ensuring the safety of language models (LMs) while maintaining their usefulness remains a critical challenge in AI alignment. Current approaches rely...
Adam Elaoumari
The purpose of this project is to assess how well defenders can detect DNS-over-HTTPS (DoH) file exfiltration, and which evasion strategies can be...
Kanchon Gharami, Sanjiv Kumar Sarkar, Yongxin Liu +1 more
Large Language Models (LLMs) like ChatGPT are now widely used in writing and reviewing scientific papers. While this trend accelerates publication...
Jaykumar Kasundra, Anjaneya Praharaj, Sourabh Surana +11 more
Safeguarding large language models (LLMs) against unsafe or adversarial behavior is critical as they are increasingly deployed in conversational and...
Aaron Chan, Alex Ding, Frank Chen +3 more
The rapid integration of Large Language Models (LLMs) into decentralized physical infrastructure networks (DePIN) is currently bottlenecked by the...
Honglin Mu, Jinghao Liu, Kaiyang Wan +4 more
Large Language Models (LLMs) excel at text comprehension and generation, making them ideal for automated tasks like code review and content...
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 4,319+ 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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