AI Threat Alert indexes 3,371+ 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 181–200 of 235 papers
Clear filtersZhida He, Xia Hu, Baichen Le +20 more
Frontier large language models (LLMs) are increasingly integrated into scientific workflows, yet their growing biological capabilities may outpace...
Yuyang Xue, Feng Chen, Zhihua Liu +4 more
Concept erasure aims to remove semantic concepts from a trained generative model and is increasingly important for responsible AI deployment....
Habibur Rahaman, Qipan Xu, Zafaryab Haider +3 more
Modern machine learning (ML) pipelines depend heavily on third party libraries for graph compilation and hardware acceleration. While current...
Xingfu Zhou, Pengfei Wang, Yuan Zhou +2 more
Agentic retrieval-augmented generation (RAG) systems increasingly retrieve external evidence and orchestrate tools for knowledge-intensive...
Yukai Zhou, Feiyang Lu, Xiaokai Mao +2 more
Jailbreak attacks on large language models are usually evaluated by attacker-centric metrics such as attack success rate (ASR), yet an attack that...
Haoyan Luo, Mateo Espinosa Zarlenga, Mateja Jamnik
Sparse autoencoders (SAEs) decompose language model activations into sparse features, but standard SAEs encode each token independently and do not...
Paul Kassianik, Blaine Nelson, Yaron Singer
Security-agent evaluations commonly measure peak offensive capability under generous inference budgets, emphasizing vulnerability discovery, exploit...
Christos Korgialas, Gabriel Lee Jun Rong, Dion Jia Xu Ho +3 more
The reliability of deepfake detectors frequently degrades under black-box adversarial transfer, as these models often rely on fragile,...
Hamid Dashtbani, Mehdi Dousti Gandomani, AmirMahdi Sadeghzadeh
Machine learning models are increasingly adapted in various domains. However, adversarial examples pose a significant threat to the reliable...
Hsiu-Chi Tsai, Chia-Tung Chung
Onboard cloud-native runtimes for satellites are emerging on multiple tracks (ORCHIDE, Axiom Space's AxDCU-1, Kepler's Jetson nodes), but each...
Darius Lim, Nathan Leow, Xin Wei Chia
Transcoders have recently emerged as a promising approach for mechanistic interpretability (MI), enabling circuit-level analysis of model behaviour....
Jifeng Gao, Kang Xia, Yi Zhang +5 more
Persistent external memory enhances agent continuity but introduces persistent security vulnerabilities: adversarial content can be injected via...
Manuel Israel Cázares
Large language models (LLMs) exhibit a well-documented gap between latent capability and consistent activation: the router hypothesis posits that...
Soham Gadgil, David Alexander, Sai Sunku +1 more
A growing class of agentic systems maintain persistent state across sessions through memory files, behavioral preferences, and knowledge bases. While...
Preeti Ravindra, Rahul Tiwari, Vincent Wolowski
AI software development agents are increasingly capable of modifying infrastructure and security critical systems, creating risks where an agent...
Yijiang Li, Huiqi Zou, Bingyang Wang +1 more
Multi-modal Large Language Models (MLLMs) have made substantial advances on benchmarks, yet their real-world effectiveness remains uncertain. This...
Rabimba Karanjai, Yang Lu, Hemanth Hegadehalli Madhavarao +2 more
Large Language Models are increasingly deployed in Security Operations Centers for log analysis tasks including summarization, alert triage, and...
Maxime Heuillet, Sharadind Peddiraju
Data science tasks span from closed-ended information extraction to open-ended analysis, presenting significant challenges for automation. Recent AI...
Taksch Dube
Advertisers delegate bidding to autobidders; users delegate tasks to language-model agents. A person describes what they want to an automated proxy...
Genglin Liu, Muye Zhang, Krishnamurthy Viswanathan +5 more
Multimodal Large Language Models (MLLMs) are increasingly deployed for nuanced content safety and moderation tasks, yet they remain vulnerable to...
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 3,371+ 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.
Get breaking CVE alerts, compliance reports (ISO 42001, EU AI Act), and CISO risk assessments for your AI/ML stack.
Start 14-Day Free Trial
AI Threat Alert