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 3461–3480 of 3,771 papers
João A. Leite, Arnav Arora, Silvia Gargova +5 more
Large Language Models (LLMs) can generate human-like disinformation, yet their ability to personalise such content across languages and demographics...
Tuan T. Nguyen, John Le, Thai T. Vu +2 more
Large language models (LLMs) achieve impressive performance across diverse tasks yet remain vulnerable to jailbreak attacks that bypass safety...
Ruben Belo, Marta Guimaraes, Claudia Soares
Large Language Models are susceptible to jailbreak attacks that bypass built-in safety guardrails (e.g., by tricking the model with adversarial...
Siyuan Li, Aodu Wulianghai, Xi Lin +4 more
With the increasing integration of large language models (LLMs) into open-domain writing, detecting machine-generated text has become a critical task...
Masoud Makrehchi
Artificial Intelligence (AI) has emerged as both a continuation of historical technological revolutions and a potential rupture with them. This paper...
Francesco Giarrusso, Olga E. Sorokoletova, Vincenzo Suriani +1 more
Jailbreaking techniques pose a significant threat to the safety of Large Language Models (LLMs). Existing defenses typically focus on single-turn...
Daniel Pulido-Cortázar, Daniel Gibert, Felip Manyà
Over the last decade, machine learning has been extensively applied to identify malicious Android applications. However, such approaches remain...
Sifan Li, Hongkai Chen, Yujun Cai +4 more
Vision Language Models (VLMs) have achieved impressive progress in multimodal reasoning; yet, they remain vulnerable to hallucinations, where outputs...
Ozan K. Tonguz, Federico Taschin
One of the major problems in Machine Learning (ML) and Artificial Intelligence (AI) is the fact that the probability distribution of the test data in...
Blazej Manczak, Eric Lin, Francisco Eiras +2 more
Large language models (LLMs) are rapidly transitioning into medical clinical use, yet their reliability under realistic, multi-turn interactions...
Yuqi Jia, Yupei Liu, Zedian Shao +2 more
Prompt injection attacks deceive a large language model into completing an attacker-specified task instead of its intended task by contaminating its...
Dongsen Zhang, Zekun Li, Xu Luo +3 more
The Model Context Protocol (MCP) standardizes how large language model (LLM) agents discover, describe, and call external tools. While MCP unlocks...
Bowen Fan, Zhilin Guo, Xunkai Li +5 more
Graph Neural Networks (GNNs) have become a pivotal framework for modeling graph-structured data, enabling a wide range of applications from social...
Xiaoxue Ren, Penghao Jiang, Kaixin Li +6 more
Web applications are prime targets for cyberattacks as gateways to critical services and sensitive data. Traditional penetration testing is costly...
Harsh Kasyap, Minghong Fang, Zhuqing Liu +2 more
Federated learning (FL) is a privacy-preserving machine learning technique that facilitates collaboration among participants across demographics. FL...
Han Zhu, Juntao Dai, Jiaming Ji +8 more
With the widespread use of multi-modal Large Language models (MLLMs), safety issues have become a growing concern. Multi-turn dialogues, which are...
Zhenyu Mao, Jacky Keung, Fengji Zhang +3 more
The increasing demand for software development has driven interest in automating software engineering (SE) tasks using Large Language Models (LLMs)....
Lipeng He, Vasisht Duddu, N. Asokan
Chatbot providers (e.g., OpenAI) rely on tiered subscription schemes to generate revenue, offering basic models for free users, and advanced models...
Deeksha Hareesha Kulal, Chidozie Princewill Arannonu, Afsah Anwar +2 more
Phishing remains a critical cybersecurity threat, especially with the advent of large language models (LLMs) capable of generating highly convincing...
Shuo Chen, Zonggen Li, Zhen Han +7 more
Deep Research (DR) agents built on Large Language Models (LLMs) can perform complex, multi-step research by decomposing tasks, retrieving online...
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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AI Threat Alert