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 121–140 of 407 papers
Clear filtersPurva Chiniya, Kevin Scaria, Sagar Chaturvedi
Large language models (LLMs) remain susceptible to jailbreak and direct prompt-injection attacks, yet the strongest defensive filters frequently...
Zijun Wang, Haoqin Tu, Letian Zhang +11 more
OpenClaw, the most widely deployed personal AI agent in early 2026, operates with full local system access and integrates with sensitive services...
Ayush Garg, Sophia Hager, Jacob Montiel +5 more
Security teams face a challenge: the volume of newly disclosed Common Vulnerabilities and Exposures (CVEs) far exceeds the capacity to manually...
Shams Tarek, Dipayan Saha, Khan Thamid Hasan +3 more
The increasing complexity of modern system-on-chip designs amplifies hardware security risks and makes manual security property specification a major...
Bowen Wei, Yunbei Zhang, Jinhao Pan +5 more
Personal AI agents like OpenClaw run with elevated privileges on users' local machines, where a single successful prompt injection can leak...
Manoj Parmar
World models -- learned internal simulators of environment dynamics -- are rapidly becoming foundational to autonomous decision-making in robotics,...
Saeid Jamshidi, Negar Shahabi, Foutse Khomh +2 more
Software-Defined Networking (SDN) is increasingly adopted to secure Internet-of-Things (IoT) networks due to its centralized control and programmable...
Yishun Wang, Wenkai Li, Xiaoqi Li +3 more
Smart contracts are self-executing programs that manage financial transactions on blockchain networks. Developers commonly rely on third-party code...
Miles Farmer, Ekincan Ufuktepe, Anne Watson +4 more
Large Language Models (LLMs) have emerged as a popular choice in vulnerability detection studies given their foundational capabilities, open source...
Osama Wehbi, Sarhad Arisdakessian, Omar Abdel Wahab +3 more
Backdoor attacks pose a significant threat to the integrity and reliability of Artificial Intelligence (AI) models, enabling adversaries to...
Aymen Lassoued, Nacef Mbarek, Bechir Dardouri +3 more
Vulnerability detection in C programs is a critical challenge in software security. Although large language models (LLMs) achieve strong detection...
Xunguang Wang, Yuguang Zhou, Qingyue Wang +5 more
Large language models (LLMs) increasingly rely on explicit chain-of-thought (CoT) reasoning to solve complex tasks, yet the safety of the reasoning...
Cristian Lupascu, Alexandru Lupascu
Large Language Model based agents increasingly operate in high stakes, multi turn settings where factual grounding is critical, yet their memory...
Yuxiao Li, Alina Fastowski, Efstratios Zaradoukas +2 more
Activation steering has emerged as a powerful tool to shape LLM behavior without the need for weight updates. While its inherent brittleness and...
Ao Ding, Hongzong Li, Zi Liang +5 more
Large language models (LLMs) are increasingly deployed on edge devices under strict computation and quantization constraints, yet their security...
Rui Yang Tan, Yujia Hu, Roy Ka-Wei Lee
Multimodal Large Language Models (MLLMs) extend text-only LLMs with visual reasoning, but also introduce new safety failure modes under visually...
Xinyue Liu, Niloofar Mireshghallah, Jane C. Ginsburg +1 more
Frontier LLM companies have repeatedly assured courts and regulators that their models do not store copies of training data. They further rely on...
Anders Giovanni Møller, Elisa Bassignana, Francesco Pierri +1 more
The ubiquity of multimedia content is reshaping online information spaces, particularly in social media environments. At the same time, search is...
Shawn Li, Yue Zhao
Large language model (LLM) agents increasingly rely on external tools (file operations, API calls, database transactions) to autonomously complete...
Rohan Siva, Kai Cheung, Lichi Li +1 more
Modern machine learning systems rely on complex data engineering workflows to extract, transform, and load (ELT) data into production pipelines....
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