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 121–140 of 275 papers
Clear filtersSubek Acharya, Syed Ayon, Khaleque Md Aashiq Kamal +3 more
Machine learning models have shown exceptional accuracy for ballot classification in election systems. Recent research has demonstrated that these...
Arun B. Chandrasekaran, Damilola Popoola, Souradeep Bhattacharya +1 more
The increasing penetration of Distributed Energy Resources (DERs) in the smart grid has significantly expanded the cyber-attack surface....
Bum Jun Kim, Anastasis Kratsios
Studies of neural network generalization and robustness often summarize smoothness with a single number, such as a Lipschitz constant or a Hessian...
Shiyu Kan, Yue Qi, Yufei Han +3 more
Large language model based agents increasingly rely on external tools to invoke services and interact with web resources. These tools can evolve...
Zhihua Wang, Yunhao Feng, Yifeng Gao +4 more
AI agents often rely on persistent memory to retain user preferences and operational rules across sessions. However, this capability introduces a new...
Mingchen Li, Ren-Yi Huang, Dumindu Samaraweera +2 more
Edge deployment of Large Language Models requires optimization techniques that reduce memory, computation, and energy costs. However, preserving task...
Ezra Edelman, Benjamin Edelman, Surbhi Goel +1 more
Despite growing capabilities, LLMs remain susceptible to prompt injections: malicious inputs that can manipulate or hijack models. In particular, it...
Yubo Cui, Xianchao Guan, Zheng Zhang
Vision-language models (VLMs) exhibit strong zero-shot generalization but remain vulnerable to adversarial attacks. Existing adversarial fine-tuning...
Abdelkader Metales, Miriam Grabenheinrich
Continual learning methods for large language models increasingly decide what to learn by surprise, keeping the inputs the model finds least...
Hicham Hammouchi, Gabriele Lenzini
Large language model serving stacks, commercial inference APIs, vector databases, and agent orchestration gateways are now ex- posed on the public...
Doohyun Kim, Donghwa Kang, Mingyu Jun +1 more
Indirect prompt injection threatens local LLM agents through untrusted tool outputs, motivating defenses that suppress attacks while preserving task...
Paula Chikaodinaka Anyanwu, Valdini Douglace Lemofouet, Blessing Ngozi Uzor +7 more
As large language models (LLMs) become more widespread in Africa, their safety in low-resource languages remains largely untested since most...
Md. Robiul Islam Niloy, Abu Sayed Md. Mostafizur Rahaman
Large language models (LLMs) deployed in safety-critical settings must resist adversarial inputs designed to elicit harmful outputs. We present a...
Youngjin Jin, Seungwon Shin
Cyber threat intelligence (CTI) reports describe adversary behavior in natural language, but converting them into structured MITRE ATT&CK mappings...
Zihou Ren, Byungchul Tak, Long Wang
Large language model (LLM) agents often receive access to more files, accounts, and services than a task requires. Prompt injection turns this excess...
Xinyan Wang, Xiaogeng Liu, Jun Shao +1 more
Self-evolving personal agents act for users and learn from user feedback. However, if an agent obeys an instruction that an untrusted third party...
Bravish Ghosh
When one AI agent tells another that "the tests passed" or "a person approved this refund", the receiver has only the sender's word. A2A and MCP...
Yiran Lin, Ye Deng, Ziqi Pan +2 more
Artificial intelligence (AI) agents are increasingly explored as collaborative learning partners in mathematics education, yet it remains unclear...
Xiaogeng Liu, Xinyan Wang, Chaowei Xiao
Typed decision models are an emerging class of language models that make decisions instead of generating free-text responses. They answer in a single...
Zhuoyun Li, Boxuan Wang, Jinwei Hu +2 more
Large Language Models (LLMs) are increasingly being used as cognitive interfaces that shape how users interpret evidence, communicate, and make...
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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