AI Threat Alert indexes 3,771+ 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 1–20 of 150 papers
Clear filtersJeremy Qin, David Schmotz, Derck Prinzhorn +3 more
Asynchronous monitoring, incident investigations, and compliance audits primarily rely on agent traces to reconstruct what happened. These analyses...
David Schmotz, Derck Prinzhorn, Luca Beurer-Kellner +3 more
A central concern in AI safety is that agents may treat oversight as an obstacle when it conflicts with completing their goals. We study instrumental...
Luciano Maldonado
Large language models increasingly operate as persistent assistants in user-facing, shared-session, and tool-augmented settings. When users disclose...
Saim Rehman, Muhammad Shafique
Deployment-oriented compression is attractive for resource-constrained brain--computer interfaces (BCIs), but whether it changes adversarial...
Qingyu Wu, Zeyu Feng, Yongda Yu +2 more
Prompt injection can degrade benign task performance without eliciting harmful content. Yet many attack objectives depend on task labels or...
Ruoqi Guo, Yi Liu, Gelei Deng +6 more
Detectors of alignment failures screen deployed language models and score alignment benchmarks. Most are generative judges that spend a decoding pass...
Sheng Liu, Panos Papadimitratos
Federated learning (FL) can improve world model (WM)-based autonomous driving (AD) without centralizing raw private vehicle data, but it also turns...
Yanming Xiu
Video see-through extended reality (VST XR) systems commonly use headset screenshots or captured frames as proxies for the user's first-person visual...
Daiki Chiba, Hiroki Nakano, Takashi Koide
Short claims such as not-phishing or official can change how a large language model (LLM) judges a domain name, without explicit prompt-injection...
Haoyang Li, Yaxin Xiao, Linyan Dai +5 more
Multimodal training relies on image-text corpora collected from external sources, creating opportunities for attackers to poison the data. Stealthy...
Manit Baser, Aditya Nawal, Dinil Mon Divakaran +1 more
Open-weight LLMs give downstream users control over the inference stack, but this flexibility can undermine post-release guarantees that sensitive...
Joas Antonio dos Santos Barbosa
Autonomous penetration-testing harnesses use large language models (LLMs) for reconnaissance, exploitation, and reporting, but often rely on those...
Spencer King, Zhilu Zhang, Mikhail Kuznetsov +3 more
LLM agents are deployed into infrastructure that grants them broad host authority, yet existing agent-security benchmarks and defenses operate almost...
Harsh Verma
Blockchain and artificial intelligence (AI) are converging into a single infrastructural layer for securing data sharing, model integrity, and...
Davood Wadi, Yu Ma
Consumers increasingly delegate purchasing decisions to Large Language Models (LLMs) acting as surrogate consumers. Using "Tool-Lab," an adaptation...
Asmah Muallem, Firdous Kausar, Sajid Hussain +1 more
Flipping 60\% of training labels from a single Byzantine client using label-flipping model poisoning self-degrades an attacker's own federated...
Mingyuan Li, Yanna Jiang, Guangsheng Yu +4 more
Privacy-sensitive organizations may run large language models (LLMs) in restricted or air-gapped environments while exporting selected diagnostic...
Saeedeh Lohrasbi, Mohammad Mamun, Ahmed Yehia +2 more
Multi-stage LLM-based cyber agents may complete attack workflows while remaining brittle, costly, or reliant on incorrect interpretations of...
Jian Xu
Agentic video-generation systems close a loop between a generator and a verifier: an LLM plans shots, calls a text-to-video model, and a multimodal...
Xuwei Tan, Yao Ma, Xueru Zhang
Real-time payment fraud detection is a non-stationary streaming prediction problem: adversaries adapt before supervised labels mature, and localized...
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 3,771+ 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