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 241–260 of 521 papers
Clear filtersMohan Rajagopalan, Vinay Rao
Large Language Model (LLM) applications are vulnerable to prompt injection and context manipulation attacks that traditional security models cannot...
Yuting Ning, Jaylen Jones, Zhehao Zhang +5 more
Computer-use agents (CUAs) have made tremendous progress in the past year, yet they still frequently produce misaligned actions that deviate from the...
Igor Santos-Grueiro
Safety evaluation for advanced AI systems assumes that behavior observed under evaluation predicts behavior in deployment. This assumption weakens...
Pouria Arefijamal, Mahdi Ahmadlou, Bardia Safaei +1 more
Federated learning (FL) is a decentralized learning paradigm widely adopted in resource-constrained Internet of Things (IoT) environments. These...
Liwen Wang, Zongjie Li, Yuchong Xie +4 more
The evolution of Large Language Models (LLMs) into agentic systems that perform autonomous reasoning and tool use has created significant...
Shadman Rabby, Md. Hefzul Hossain Papon, Sabbir Ahmed +3 more
Sycophancy in Vision-Language Models (VLMs) refers to their tendency to align with user opinions, often at the expense of moral or factual accuracy....
Sai Puppala, Ismail Hossain, Md Jahangir Alam +5 more
Large language models are increasingly deployed as *deep agents* that plan, maintain persistent state, and invoke external tools, shifting safety...
Kunal Pai, Parth Shah, Harshil Patel
AI agents are increasingly deployed in production, yet their security evaluations remain bottlenecked by manual red-teaming or static benchmarks that...
Xiang Li, Pin-Yu Chen, Wenqi Wei
With the rapid advancement and adoption of Audio Large Language Models (ALLMs), voice agents are now being deployed in high-stakes domains such as...
Qi Sun, Ahmed Abdo, Luis Burbano +4 more
Autonomous Vehicles (AVs), especially vision-based AVs, are rapidly being deployed without human operators. As AVs operate in safety-critical...
Haoyang Hu, Zhejun Jiang, Yueming Lyu +3 more
Retrieval-augmented generation (RAG) is increasingly deployed in real-world applications, where its reference-grounded design makes outputs appear...
Yi Liu, Zhihao Chen, Yanjun Zhang +5 more
Third-party agent skills extend LLM-based agents with instruction files and executable code that run on users' machines. Skills execute with user...
Navita Goyal, Hal Daumé
Model steering, which involves intervening on hidden representations at inference time, has emerged as a lightweight alternative to finetuning for...
José Ramón Pareja Monturiol, Juliette Sinnott, Roger G. Melko +1 more
Machine learning in clinical settings must balance predictive accuracy, interpretability, and privacy. Models such as logistic regression (LR) offer...
Ruixin Yang, Ethan Mendes, Arthur Wang +4 more
Vision-language models (VLMs) have demonstrated strong performance in image geolocation, a capability further sharpened by frontier multimodal large...
Casey Ford, Madison Van Doren, Emily Dix
Multimodal large language models (MLLMs) are increasingly deployed in real-world systems, yet their safety under adversarial prompting remains...
Debargha Ganguly, Sreehari Sankar, Biyao Zhang +8 more
Current approaches to LLM safety fundamentally rely on a brittle cat-and-mouse game of identifying and blocking known threats via guardrails. We...
Omar Abdelnasser, Fatemah Alharbi, Khaled Khasawneh +2 more
Safety alignment in Language Models (LMs) is fundamental for trustworthy AI. However, while different stakeholders are trying to leverage Arabic...
Tomer Kordonsky, Maayan Yamin, Noam Benzimra +2 more
LLMs are increasingly used for code generation, but their outputs often follow recurring templates that can induce predictable vulnerabilities. We...
Najmul Hasan, Prashanth BusiReddyGari
The Uniform Resource Locator (URL), introduced in a connectivity-first era to define access and locate resources, remains historically limited,...
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