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 21–40 of 1,072 papers
Clear filtersAkihisha Fujiyama, Niwase Shamim
AI is increasingly used to automate computer security testing, and the tools must decide for themselves whether an attack succeeded. A finding that a...
Rohit Patel, Susil Kumar Mohanty, Jeenal Chaudhary
Root cause analysis at a remote site is slow: evidence is scattered across pod logs, Kubernetes events and cluster-level objects, and many operators...
Muhammad Abdullah Sohail
The Model Context Protocol (MCP) has emerged as the dominant interface for connecting autonomous agents to external data sources and execution...
Elia Nikolaou, Magnus Wiik Eckhoff, Robert Flood +3 more
LLM-based agents generate and execute multi-step plans that invoke external tools which can access private data or execute commands. In this setting,...
Hasnain Irshad, Anam Mughees, Neelam Mughees +2 more
Agentic browsers can execute security-sensitive actions under a user's authenticated session, making indirect prompt injection and deceptive...
Franziska Roesner, Tadayoshi Kohno
Thompson's "Reflections on Trusting Trust" showed that a compiler can be poisoned to reinsert its own backdoor, so that even recompiling clean source...
Lehao Lin, Yuheng Cheng, Guolong Liu +9 more
Long-horizon reasoning is vulnerable to early errors that compromise later decisions. We present QART, the Quantum-Augmented Reasoning Transformer, a...
Zhuoang Cai
As Large Language Models (LLMs) increasingly serve as primary knowledge retrieval interfaces, their robustness against \textit{persuasion...
Kairong Li, Zhikun Zhang, Xiao Ren +1 more
LLM watermarking helps trace the origin of generated text, but faces stealing attacks that recover watermark information, scrubbing attacks that...
Sri Hrushikesh Varma Bhupathiraju, Tetsu Ishizue, Nicholas U. Costagliola +4 more
Stereo cameras are integrated into autonomous systems such as self-driving cars, drones, and robots to offer precise depth estimation in a...
Aman Priyanshu, Supriti Vijay, Kimia Majd +8 more
Language-model agents increasingly operate over complete software repositories, yet cybersecurity evaluations primarily measure whether they can...
Xingyu Lyu, Jiayimei Wang, Jianfeng He +3 more
Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for improving the quality of generated contents of Large Language Models...
Toshif Khan, Muhammad Abusaqer
Data poisoning corrupts training data to degrade a model or to plant attacker-controlled behavior. This study evaluates two representative...
Mohammad Farhad, Shuvalaxmi Dass
Large language models (LLMs) are increasingly used to generate and assist with software development, yet existing vulnerability datasets largely...
Jessica Pourleyli, Maitreyee Das Urmi, Glaucia Melo
Advances in large language models (LLMs) fuel the quest for scalable methods to assess the security of generated and security-sensitive software....
Jinyang Li, Mingyu Guo, Hung X. Nguyen
Large language models (LLMs) have been ex- ploited to generate malware, but the effective- ness of guardrails for code generation secu- rity remains...
Viet K. Nguyen, Mohammad I. Husain
Agentic AI frameworks let a language model plan, keep memory, and call tools that reach real files, mail, and services. Most of these agents also...
Yixuan Liu, Zilong Zhen, Yin Wu +1 more
As Large Language Model (LLM) agents increasingly automate offensive operations across the cyber kill chain, their efficacy in complex local...
Jiahao Shi, Edward Tsien, Yifeng Di +10 more
The software supply chain has become an increasingly exposed attack surface because of its reliance on intricate yet fragile dependencies. Existing...
Yage Zhang, Xinyue Shen, Yukun Jiang +2 more
Otome games, a romance simulation genre primarily targeting female, have emerged as a major force in the global gaming market, attracting hundreds of...
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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