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 21–40 of 808 papers
Clear filtersEric Wallace, Christopher A. Choquette-Choo, Nikhil Kandpal +15 more
We introduce \textbf{GPT-Red}, an automated red-teaming agent that is trained to discover novel prompt injection attacks against frontier LLMs. The...
Yu Yan, Jiahao Chen, Siqi Lu +6 more
Large Language Models (LLMs) have been widely applied in high-stakes decision-making scenarios such as corporate strategy, and users are increasingly...
Zixuan Wu, Cristina Nita-Rotaru
Large language models are increasingly deployed for security-sensitive tasks such as vulnerability detection and code review. Their reliance on...
Junchi Liao, Jiawen Deng, Fuji Ren
Visible tests are a common gate for LLM-generated code, but passing them does not certify specification correctness. We study a deployment-like...
Lynn Delcon, Andres Algaba, Vincent Ginis
Perturbation techniques that turn unsuccessful jailbreak prompts into successful ones are continuously evolving, constituting a major security threat...
Yohann Sidot
We study a five-agent CI/CD pipeline (triage -> developer -> security-scan -> review -> approve/deploy), built from five distinct production LLMs...
SangJin Park, Myungsub Choi, Jineok Kim +1 more
LLM-agent defenses are typically evaluated one session at a time. In deployment, however, attacks can be distributed across independent agents,...
Xinting Liao, Behnoosh Zamanlooy, Masoumeh Shafieinejad +4 more
Textual Collaborative Prompt Optimization (TCPO) extends Textgrad (Yuksekgonul et al., 2025) to a decentralized setting by allowing multiple clients...
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...
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...
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...
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...
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...
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