AI Threat Alert indexes 3,795+ 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 1361–1380 of 3,795 papers
Junjie Yu, Pengrui Lu, Weiye Si +75 more
Benchmarks within the OpenClaw ecosystem have thus far evaluated exclusively assistant-level tasks, leaving the academic-level capabilities of...
Mario Rodríguez Béjar, Francisco J. Cortés-Delgado, S. Braghin +1 more
Large language models (LLMs) remain vulnerable to jailbreak attacks that bypass safety alignment and elicit harmful responses. A growing body of work...
Arne Roszeitis, Bartosz Burgiel, Victor Jüttner +1 more
Smart devices, such as light bulbs, TVs, fridges, etc., equipped with computing capabilities and wireless communication, are part of everyday life in...
Wenjing Duan, Qi Zhou, Yuanfan Li
Machine-generated text (MGT) detection is critical for regulating online information ecosystems, yet existing detectors often underperform in...
Judith Sáinz-Pardo Díaz, Álvaro López García
The growing development of artificial intelligence based solutions, together with privacy legislation, has driven the rise of the so-called privacy...
Adel ElZemity, Budi Arief, Shujun Li +6 more
Bare-metal operational technology (OT) devices -- especially the microcontrollers running Modbus/TCP and CoAP at the base of industrial control...
Karima Makhlouf, Lamiaa Basyoni, Syed Khaderi +4 more
Large language models (LLMs) are increasingly deployed in interactive and retrieval-augmented settings, raising significant privacy concerns. While...
Ji Guo, Xiaolong Qin, Cencen Liu +3 more
Vision-Language Models (VLMs) have achieved remarkable success in tasks such as image captioning and visual question answering (VQA). However, as...
Mingyu Luo, Zihan Zhang, Zesen Liu +7 more
Bring-Your-Own-Key (BYOK) agent architectures let users route LLM traffic through third-party relays, creating a critical integrity gap: a malicious...
Wenwei Zhao, Xiaowen Li, Yao Liu +1 more
Federated learning (FL) is vulnerable to poisoning attacks, where malicious clients upload manipulated updates to degrade the performance of the...
Debeshee Das, Julien Piet, Darya Kaviani +3 more
Memory systems enable otherwise-stateless LLM agents to persist user information across sessions, but also introduce a new attack surface. We...
Sadia Asif, Mohammad Mohammadi Amiri
Fine-tuning safety-aligned language models for downstream tasks often leads to substantial degradation of refusal behavior, making models vulnerable...
Jiajia Li, Xiaoyu Wen, Zhongtian Ma +3 more
The growing capabilities of large language models (LLMs) have driven their widespread deployment across diverse domains, even in potentially...
George Fatouros, Georgios Makridis, John Soldatos +18 more
European financial institutions face mounting regulatory pressure while their security operations centres remain constrained not by data or staffing...
Mohd Ruhul Ameen, Md Takrim Ul Alam, Akif Islam
Static Application Security Testing tools help developers find security vulnerabilities before release, but they often produce many false positives....
Zhiyang Dai, Yansong Gao, Boyu Kuang +5 more
Contrastive learning (CL) reduces annotation cost via auto-derived supervisory signals. Since large-scale in-house CL datasets are infeasible,...
Huining Cui, Wei Liu
Retrieval-augmented generation (RAG) improves factual grounding by conditioning large language models on retrieved evidence, but it also opens a...
Yanting Wang, Chenlong Yin, Ying Chen +1 more
Long-context large language models (LLMs)-for example, Gemini-3.1-Pro and Qwen-3.5-are widely used to empower many real-world applications, such as...
Prashant Kulkarni
Multi-turn prompt injection follows a known attack path -- trust-building, pivoting, escalation but text-level defenses miss covert attacks where...
Bowen Sun, Chaozhuo Li, Yaodong Yang +2 more
Decompositional jailbreaks pose a critical threat to large language models (LLMs) by allowing adversaries to fragment a malicious objective into a...
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 3,795+ 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