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 221–240 of 257 papers
Clear filtersNing Ni, Yingjie Lao
Large language models (LLMs) outperform earlier architectures on generative inference and long-context tasks, but their large size introduces...
Manoj Parmar
Neuro-symbolic AI (NeSy) pairs neural perception with symbolic reasoning, making it attractive for high-stakes domains where explainability and...
Junfeng Guo Heng Huang
While real-world applications of reinforcement learning (RL) are becoming increasingly popular, the security of RL systems deserve more attention and...
Mayank Raj, Nathaniel D. Bastian, Lance Fiondella +1 more
Network Intrusion Detection Systems (NIDS) heavily utlize Machine Learning (ML) but ML models can be manipulated via adversarial attacks. These...
Jun Wen Leong
We present an online monitoring system for distributional shift in deployed safety classifiers, using calibrated sequential statistics to detect when...
Kazuki Iwahana, Masaru Matsubayashi, Takuma Koyama +3 more
Backdoor attacks pose a serious threat to the safety and reliability of Large Language Models (LLMs), as they cause models to behave normally on...
Zexiao Zou, Zhiqiang Wang, Baoxu Liu +2 more
As an effective anti-censorship mechanism, network covert channels can provide data privacy protection and ensure communication security. However,...
Bulat Nutfullin, Vladimir Evgrafov, Dmitry Namiot
Multimodal large language models (MLLMs) now appear in safety-critical applications, but the visual channel leaves them open to adversarial attacks...
Hankun Lin, Ruqi Zhang
Ensuring the reliability of Large Language Models (LLMs) under distribution drift requires inference-time adaptation. While inference-time alignment...
Tiejin Chen, Pingzhi Li, Kaixiong Zhou +2 more
Privacy risks in text-only Large Language Models (LLMs) are well studied, particularly their tendency to memorize and leak sensitive information....
Arun Malik
Cloud network infrastructure at hyperscale presents unique operational challenges where traditional human-driven incident response cannot keep pace...
Yanyan Luo, Xue Han, Ruiqiao Bai +10 more
Large Language Models (LLMs) have enabled increasingly personalized interactions by adapting to users' preferences, contexts, and long-term...
Bernhard Kneip, Nhien-An Le-Khac, Hong-Hanh Nguyen-Le
Forensic analysis of web server logs demands both accurate detection and human-readable explanations that can satisfy legal requirements. We present...
Weilin Lin, Ziqi Lin, Zhenxing Zhou +4 more
Image safety classifiers serve as a critical component of contemporary content moderation systems on the internet. However, their resilience against...
Virginia Ceccatelli, Yejin Jeon, David Ifeoluwa Adelani
Large audio language models (LALMs) are increasingly deployed in real-world applications, yet their safety alignment is still primarily evaluated on...
Minseok Choi, Seungbin Yang, Dongjin Kim +5 more
Despite advances in safety alignment, large language models remain vulnerable to continuously evolving jailbreaks. Existing fine-tuned safety...
Hiskias Dingeto, Will Leeney
Indirect prompt injection in tool-use agents is a concrete production threat: LLM agents read from integrations (third-party services such as Gmail,...
Syafiq Al Atiiq, Chun Zhou, Christian Gehrmann
Large language models (LLMs) can detect software vulnerabilities, but how do they actually identify vulnerable code? We address this question using...
Zhibo Zhang, Yuxi Li, Zhen Ouyang +2 more
Mixture-of-Experts (MoE) LLMs rely on sparse, router-driven expert activation, yet how safety alignment interacts with routed expert specialization...
Runang He, Tongya Zheng, Huiling Peng +6 more
Ever-evolving transaction patterns have significantly hindered anomaly detection on emerging cryptocurrency blockchains due to the vast number of...
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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