AI Threat Alert indexes 3,771+ 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 341–353 of 353 papers
Clear filtersRui Wu, Yihao Quan, Zeru Shi +3 more
Safety-aligned Large Language Models (LLMs) still show two dominant failure modes: they are easily jailbroken, or they over-refuse harmless inputs...
Lesly Miculicich, Mihir Parmar, Hamid Palangi +4 more
The deployment of autonomous AI agents in sensitive domains, such as healthcare, introduces critical risks to safety, security, and privacy. These...
Yuhao Sun, Zhuoer Xu, Shiwen Cui +4 more
Large Language Models (LLMs) have achieved remarkable progress across a wide range of tasks, but remain vulnerable to safety risks such as harmful...
Guobin Shen, Dongcheng Zhao, Haibo Tong +3 more
Ensuring Large Language Model (LLM) safety remains challenging due to the absence of universal standards and reliable content validators, making it...
Ayda Aghaei Nia
Completely Automated Public Turing tests to tell Computers and Humans Apart (CAPTCHAs) are a foundational component of web security, yet traditional...
Zherui Li, Zheng Nie, Zhenhong Zhou +7 more
The rapid advancement of Diffusion Large Language Models (dLLMs) introduces unprecedented vulnerabilities that are fundamentally distinct from...
Gauri Kholkar, Ratinder Ahuja
As autonomous AI agents are used in regulated and safety-critical settings, organizations need effective ways to turn policy into enforceable...
Yuqiao Meng, Luoxi Tang, Feiyang Yu +4 more
Large language models (LLMs) are increasingly used to help security analysts manage the surge of cyber threats, automating tasks from vulnerability...
Zeyu Shen, Basileal Imana, Tong Wu +3 more
Retrieval-Augmented Generation (RAG) enhances Large Language Models by grounding their outputs in external documents. These systems, however, remain...
Charles E. Gagnon, Steven H. H. Ding, Philippe Charland +1 more
Binary code similarity detection is a core task in reverse engineering. It supports malware analysis and vulnerability discovery by identifying...
Anton Korznikov, Andrey Galichin, Alexey Dontsov +3 more
Activation steering is a promising technique for controlling LLM behavior by adding semantically meaningful vectors directly into a model's hidden...
Jaehan Kim, Minkyoo Song, Seungwon Shin +1 more
Recent large language models (LLMs) have increasingly adopted the Mixture-of-Experts (MoE) architecture for efficiency. MoE-based LLMs heavily depend...
Wei Huang, De-Tian Chu, Lin-Yuan Bai +6 more
Modern email spam and phishing attacks have evolved far beyond keyword blacklists or simple heuristics. Adversaries now craft multi-modal campaigns...
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 3,771+ 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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