AI Threat Alert indexes 3,406+ 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 1,315 papers
Clear filtersChengheng Li-Chen, Kyuhee Kim
Regulatory regimes such as the EU AI Act mandate machine-readable marking of synthetic text, but existing watermark detectors rely on the generating...
Tanveer Ahmed, Seyedali Pourmoafil
Phishing emails remain one of the most persistent cybersecurity threats, and machine-learning classifiers are widely used to detect them. Most...
Daekwon Pi, Sangho Lee, Young Hun Lee +1 more
While modern vehicle security depends on effective Cyber Threat Intelligence (CTI) synthesis, current automated tools struggle with unstructured data...
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...
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...
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...
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...
Taksch Dube
Advertisers delegate bidding to autobidders; users delegate tasks to language-model agents. A person describes what they want to an automated proxy...
Jarosław A. Miszczak
The potential capabilities of quantum computers motivated the development of cryptographic protocols suitable for securing communication against...
Junhui Wang, Hangtao Zhang, Zhirun Zheng +5 more
Large language models (LLMs) are increasingly deployed as purpose-specific agents to handle domain-specific tasks such as customer service and code...
Igor 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...
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 3,406+ 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