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 241–260 of 4,319 papers
Hao Li, Tongyan Hu, Weidi Luo +8 more
Agent systems are shifting from task-scoped tool-use agents to persistent, personalized assistants such as OpenClaw. These claw-style agents maintain...
Nataša Krčo, Euodia Dodd, Peter Romov +1 more
Speech models are increasingly used as an interface to AI systems, from transcription tools to voice agents. Existing audio backdoors require an...
Shruthi Gowda, Nikita Jain, Elahe Arani +1 more
Deep neural networks have achieved remarkable success on vision benchmarks, yet they continue to struggle with many generalization challenges....
Yuyang Zhang, Yihui Jin, Kangjie Chen +2 more
With the advancement of powerful AI chips, on-device machine learning is gaining traction for its low latency and privacy protection. However, when...
Osama Shabih, Dayam Nadeem, Shahab Saquib Sohail +1 more
Large Language Models (LLMs) have significantly advanced natural language processing and artificial intelligence applications through their...
Haonan Huang, Chengkun Cai
Language models can change a committed answer to accommodate a user's opinion or pushback without new evidence. However, when this behaviour develops...
Leon Tu Nguyen, Rohan Menon, Rafael Tappe Maestro +2 more
Jailbreak methods are commonly compared using Attack Success Rate (ASR), but these comparisons depend on how responses are generated and judged. In...
Rohan Menon, Chih-Hong Cheng
Attack success rate (ASR) evaluates jailbreaks against large language models without accounting for repeated calls to the victim model. Yet each...
Nam Hoang Thanh, Trung Pham Duy, Lam Bui Thu
Machine-learning (ML) malware detectors are known to be vulnerable to adversarial samples. Generative adversarial networks (GANs) such as MalGAN,...
Mengxiao Wang, Nitesh Saxena
Large language model (LLM) agents now trade real capital using news as evidence, yet they cannot reliably tell fabricated headlines from real news....
Walid El Maouaki, Mohammad Izen, Nouhaila Innan +2 more
Quantum machine-learning (QML) models are increasingly considered for security applications, yet their adversarial robustness in malware analysis...
Anastasiia Kucherenko, Francois Brouchoud, Dimitri Percia David +1 more
Confidentiality requirements increasingly push legal professionals toward small, on-device LLMs that keep case data in-house, used for everyday tasks...
Manohar Kaul, Sadbhavana Babar, Aditya Ramesh +4 more
Adversarial vulnerability persists across modern vision architectures from CNNs to vision language models (VLMs), yet existing detection methods rely...
Yinxiang Wang, Zehua Ma, Kejiang Chen +2 more
LLM-based guardrails are effective against indirect prompt injection (IPI), but their performance depends heavily on model capability: as the...
Mirza Masfiqur Rahman, MD Ajwad Akil, Imtiaz Karim +3 more
Code generated by large language models often passes every functional test while remaining insecure. These security hallucinations are common,...
Yuchen Chen, Wei Cheng, Yuan Xiao +7 more
Retrieval-Augmented Generation (RAG) enables large language models to access external knowledge during inference, but its reliance on external...
Zhicheng Fang, Han Sun, Jiachen Lei +5 more
As jailbreak attacks against large language models (LLMs) become increasingly diverse, red-teaming has come to rely on large libraries of jailbreak...
Chang Zhou, Jiarun Zhou, Xingtong Yu +3 more
Generator--critic trading systems pair an LLM-based trader that makes trading decisions with a red team agent that evaluates these decisions and...
Haoran Xue, Nikta Akbarpour, Gias Uddin +1 more
Large language model (LLM) coding agents, such as Claude Code, are increasingly used for security auditing, with task-specific skills providing...
Nanjun Zhou, Yiming Li, Yukun Chen +3 more
Tool poisoning has emerged as a critical security threat to LLM agents, where adversaries embed malicious instructions in tool descriptions or...
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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