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 81–100 of 150 papers
Clear filtersJing Guan, Yachao Yang, Zhaoliang Liu +3 more
Large language models remain fragile against malicious fine-tuning, motivating training-time defenses against harmful persona drift. Preventative...
Hanyi Zhou, Chenyang Li, Yuanzhe Pang +3 more
Trusted Execution Environments (TEEs) offer a promising mechanism for safeguarding the intellectual property of on-device Large Language Models...
Arnab Chattopadhayay, Debdipta Halder
Large language model agents produce fluent action sequences across a wide range of tasks, yet they fail in characteristic ways once the environment...
Rafael M. Mamede, Pedro C. Neto, Ana F. Sequeira
Deepfake detectors remain vulnerable to transfer-based black-box attacks, in which adversarial examples are generated on a source surrogate model and...
Jinyang Li, Mingyu Guo, Hung X. Nguyen
Large language models (LLMs) have been ex- ploited to generate malware, but the effective- ness of guardrails for code generation secu- rity remains...
Yixuan Liu, Zilong Zhen, Yin Wu +1 more
As Large Language Model (LLM) agents increasingly automate offensive operations across the cyber kill chain, their efficacy in complex local...
Sarah Meriem Ourari
Software supply chain security has become increasingly critical due to the widespread reliance on third-party dependencies and the growing attack...
Ayan Roy, Kaustuvi Basu
Agentic AI systems with persistent memory introduce a distinct attack surface known as memory poisoning, in which adversarially crafted content is...
Iliano Fasolino
Retrieval-augmented generation (RAG) grounds a language model in retrieved documents, which reduces hallucination but creates a new attack surface:...
Hyun Gu Kang, Daniil Gurgurov, Tanja Baeumel +2 more
This study examines the compositionality of steering vectors for language and behavioral control in large language models. Focusing on language,...
Andrea Ponte, Luca Demetrio, Luca Oneto +2 more
Industrial Windows malware detectors are commonly described as Compound AI Systems composed of multiple heterogeneous components, including...
Han Jin
We introduce HoneyRoute, an inference-serving layer that detects whether an incoming request is malicious and, if so, routes it to a dedicated...
Yongxi Zhou, Wenbo Ye, Yuanzhe Liu +2 more
Automatic safety judges -- systems such as Llama Guard or a GPT-4o grading prompt that decide whether a model's reply is harmful -- produce the...
Xinhong Xie, Piyush Nagasubramaniam, Neeraj Karamchandani +1 more
Large language model (LLM) agents are increasingly employed for offensive cybersecurity tasks such as automated vulnerability discovery,...
Yuji Zhang, Weibing Wang, Cheng Qian +5 more
Updating a language model's knowledge through fine-tuning is essential for keeping its outputs current, yet can also induce factual forgetting and...
Yage Zhang, Xinyue Shen, Yukun Jiang +2 more
Otome games, a romance simulation genre primarily targeting female, have emerged as a major force in the global gaming market, attracting hundreds of...
Xiao Ma, Hong Shen, Hui Tian +2 more
This paper proposes a robust decentralized personalized federated learning method R-DPFL, that enables clients to reduce the impact of Byzantine...
Branka Stojanović, Andreas Flatscher, Michael Somma
Anomaly-based intrusion detection systems in industrial control systems (ICS) and operational technology (OT) environments are increasingly required...
Uday Vallabhaneni, Cassie L. Cagwin, David J. Wild
Large language model (LLM) agents are increasingly proposed as autonomous SOC analysts, but two limitations make them unreliable at enterprise scale:...
Lingyu Li, Yan Teng, Yingchun Wang +1 more
Aligning large language models (LLMs) is essential for their safe deployment. Current alignment methods mainly optimize observable responses, yet...
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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