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 644 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...
Lukáš Brůna, Robert Bridges, Adam Ek
Large Language Models (LLMs) consume and produce a single sequence of text; hence, if text can be added to the beginning of the LLM's response, i.e.,...
Karina Elzer, Niklas Netterstrøm Johansen, Emmanouil Vasilomanolakis
Publicly exposed large language model (LLM) infrastructure creates a growing attack surface, yet real-world targeting remains poorly understood. We...
Yu-Ling Liao, Tzu-Chin Chiu, Zong-You Chen +2 more
Large audio-language models (LALMs) expand language models to process and interpret audio, but also expose them to heterogeneous audio jailbreaks. We...
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...
Siyuan Pang, Yepeng Yao, Zhengwei Jiang +2 more
The Node.js ecosystem heavily relies on NPM packages, and software supply chain attacks targeting malicious NPM packages are rampant. Malicious code...
Harsh Verma
Blockchain and artificial intelligence (AI) are converging into a single infrastructural layer for securing data sharing, model integrity, and...
Tiantong Wu, Wei Yang Bryan Lim
Most studies of prompt injection focus on generative agents, leaving their effects on models with schema-defined outputs unclear. We examine these...
Jasem Khelifi, Issam Oukhay, Ali Ouni +2 more
Agentic workflows shift software development from prompting AI agents for individual tasks to defining recurring work that agents execute...
Tianhao Chen, Yuhan Wei, Weifei Jin +3 more
Multi-passage corpus poisoning often repeats one target claim across similar documents, creating correlated lexical and semantic patterns that...
Christopher Burger, Christina Trotter, Joseph Carlisle +1 more
The rapidly advancing capabilities of vision-language models (VLMs) present a systemic challenge to academic integrity. VLMs now allow students to...
Arthur Cordeiro, Alberto Maria Mongardini, Emmanouil Vasilomanolakis
Honeypots are designed to deceive attackers, and recent work shows they can also derail autonomous LLM-based pentesters. These evaluations, however,...
Abdullahil Kafi, Alvi Ataur Khalil
Retrieval augmented generation (RAG) systems have emerged as the dominant architecture for grounding large language model (LLM) outputs in verifiable...
Ariel Flint, Luca Maria Aiello, Sara M. Constantino +2 more
As generative AI agents are deployed at scale, safety will depend not only on technical safeguards and individual model design, but also on...
Fernando Outeda, Gustavo Betarte, Juan Diego Campo +1 more
Large language models (LLMs) are increasingly deployed in production systems, raising concerns about their exposure to adversarial manipulation...
Florian Krone, Elena Hoemann, Sven Hallerbach
Neural networks, both convolution or transformer based, are essential for modern computer vision systems. However, they are vulnerable to small...
Ben Shapira, Roi Cohen, Shang-Tse Chen +1 more
With face-recognition models now embedded in everyday authentication and surveillance, recent works have pinpointed a critical weakness: these models...
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