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 181–200 of 888 papers
Clear filtersJian Yang, Haau-Sing Li, Shawn Guo +9 more
As large language models (LLMs) continue to advance in coding capabilities, their potential in cybersecurity has drawn increasing research attention,...
Kohei Yamamoto, Marie Katsurai
Attacks against Internet-connected IoT devices continue to increase; however, transforming observed attack traffic into deployable intrusion...
Basavesh Ammanaghatta Shivakumar, Swarn Priya, Peng Gao
LLM agents increasingly read untrusted content, invoke external tools, access private data, and delegate work to other agents. Harm often arises not...
Yue Li, Sudip Bhujel, Cameron Lira +2 more
Decentralized federated learning (DFL) is a promising paradigm for autonomous nodes to collaboratively train AI models without relying on a central...
Vincenzo Sammartino, Nathanael Denis, Roberto Di Pietro
X-band SAR satellites (8-12 GHz) play a critical role in disaster response, environmental monitoring, and military intelligence. Yet, they lack...
Yiting Qu, Ziqing Yang, Chi Cui +3 more
Hidden chain-of-thought (CoT) traces, especially those from frontier proprietary large reasoning models (LRMs), are valuable model assets. Yet...
Yue Wang, Yi Liu, Gelei Deng +4 more
Agent Skills extend LLM agents with reusable instruction packages that may also include scripts, resources, and service configuration. This creates a...
Jaiden Fairoze, Neal Mangaokar, Kamalika Chaudhuri +2 more
For AI agents to be useful beyond simple chat, they must hold sensitive user context such as calendars, credentials, health records, and financial...
Ye Tao, Hong Shen, Hui Tian +2 more
Gradient inversion attacks recover private training text from gradients shared in federated learning, posing a serious threat to collaborative model...
Krishna Teja Medam
A connected vehicle has roughly 100 milliseconds to decide whether an incoming Basic Safety Message is real or fabricated. If a false emergency...
Sepehr Ghaffarzadegan, Boubakr Nour, Makan Pourzandi +2 more
Mechanisms for dynamically converting cyber threat intelligence (CTI) into actionable detection capabilities are necessary due to the rapid evolution...
Ilan Zini, Boussad Addad, Katarzyna Kapusta
Vision Language Models (VLMs) have recently emerged as a critical component of multimodal AI systems, enabling joint reasoning over visual and...
Kai Li, Jong-Ik Park, Carlee Joe-Wong +2 more
Federated training enables language models to learn from distributed private text, but the server cannot directly verify the local supervision or...
Muris Sladić, Veronica Valeros, Eman Alibalić +1 more
LLM-based SSH honeypots often use closed cloud LLMs because they give strong shell realism, but cloud models create deployment problems. These...
Yajing Bai, Jinhao Duan, Jie Peng +4 more
Large language models are increasingly deployed through agent harnesses that manage tools, extensions, persistent state, permissions, and external...
Istiaque Ahmed, Afia Anjum Borsha, Ranat Das Prangon +2 more
Large Language Models (LLMs) in real-world applications often face the risks of specially crafted prompts designed to bypass the safety controls....
Enrique Barba Roque, Luís Cruz, Annibale Panichella
Background: Large Language Models (LLMs) are increasingly being applied to Software Engineering (SE) tasks, achieving high accuracy across problems...
Md Abdullahil Oaphy, Anhao Xiang, Zongxing Xie +3 more
Multimodal large language models (MLLMs) are increasingly used to interact with screenshots, scanned documents, diagrams, and other visually grounded...
Rabimba Karanjai, Yang Lu, Richard Williamson +5 more
Autonomous AI agents are emerging as interfaces for decentralized finance (DeFi) actions such as swaps, lending operations, and yield management....
Mehrdad Ghassabi
Retrieval-Augmented Generation (RAG) has significantly enhanced the performance of large language models (LLMs), yet these systems remain vulnerable...
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.
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