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 81–100 of 660 papers
Clear filtersIgor Santos-Grueiro
LLM-assisted reverse-engineering (RE) systems analyze strings, decompiler output, and tool reports derived from ttacker-controlled binaries. A binary...
Weifeng Yuan, Wenbo Guo, Feng Dong +2 more
LLM agents acquire new capabilities by downloading skills from open registries. Instead of browsing these catalogs manually, developers typically ask...
Alon Shakevsky, Corban Villa, Ion Stoica +1 more
Discovering vulnerabilities before attackers exploit them requires high recall and reliable automatic validation, but existing approaches struggle to...
A. Krylov, D. Rakhov, V. Veselova +2 more
Perceptual hash algorithms (PHAs) are widely deployed to detect image forgery under benign transformations, yet their robustness against...
Bacui Li, Chandra Thapa, Tansu Alpcan +1 more
Adversarial perturbations threaten machine learning classifiers, including variational quantum classifiers. We show that finite quantum measurement...
Junyoung Park, Namgyu Park, Sechan Lee +3 more
Modern large language models (LLMs) operate in interactive multi-turn settings, making multi-turn jailbreaking a realistic threat model and an...
Elette Boyle, MohammadTaghi Hajiaghayi, Keivan Rezaei +2 more
Model stealing attacks have recently been introduced, enabling the extraction of precise information from black-box commercial language models. In...
Eugene Ng Yi Sheng, Bingquan Shen
Self-interested agents, left unconstrained, tend toward defection in repeated social dilemmas, causing cooperative gains from trade to collapse. This...
Puji Wang, Yingchen Zhang, Ruqing Zhang +2 more
Persistent AI agents extend large language models (LLMs) beyond single-turn interaction into long-lived software systems. Unlike traditional chat...
Corban Villa, Alp Eren Ozdarendeli, Sijun Tan +1 more
Autonomous web agents promise to automate everyday browsing tasks, but inherit one of the web's oldest attack surfaces. Cross-Site Scripting proved...
Zifan Zhang, Minghong Fang, Dianwei Chen +5 more
Federated reinforcement learning (FRL) is crucial for enabling collaborative learning across multiple agents without sharing raw data, thereby...
Jennifer Za, Julija Bainiaksina, Nikita Ostrovsky +2 more
Chain-of-thought (CoT) monitoring is a promising safety mechanism for AI agents, based on the premise that visible reasoning traces can surface...
Anupam Wagle, Ifrat Ikhtear Uddin, Chaowei Zhang +1 more
Large language models (LLMs) exhibit remarkable capabilities but remain highly vulnerable to adversarial prompts and jailbreak attacks. Existing...
Phat T. Tran-Truong, Xuan-Bach Le, Minh Nhat Nguyen
Federated foundation-model adaptation increasingly relies on heterogeneous private artifacts (retrieval corpora, prompts and demonstrations, LoRA...
Aya Spira, Stav Cohen, Elad Feldman +3 more
The growing adoption of agentic LLM applications has introduced a new threat previously named as promptware. While prior work has established that...
Chethan Krishnamurthy Ramanaik, Tobias Callies, Michael Hecht +1 more
Adversarial vulnerability in deep neural networks (DNNs) has been studied from the perspectives of decision-boundary geometry, feature robustness,...
Xin Li, Jiaju Han, Ma Yaqi +5 more
Infrared vision-language models are increasingly used for perception under low-light and adverse visual conditions, yet their robustness to localized...
Cong Su, Jiaju Han, Xuemeng Sun +5 more
Vision-language models (VLMs) are increasingly deployed on infrared (IR) remote sensing imagery in security-critical settings, yet their adversarial...
Issam Seddik, Sami Souihi, Mohamed Tamaazousti +1 more
Backdoor attacks severely threaten large-scale AI models. When model owners delegate training to external compute providers within a decentralized...
Sai Varun Kodathala
AI agents issue tool calls on the basis of text they cannot verify, so any party who controls part of the context can forge the appearance 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 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.
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AI Threat Alert