AI Threat Alert indexes 3,082+ 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 1781–1800 of 3,082 papers
Nilanjana Chatterjee, Sidharatha Garg, A V Subramanyam +1 more
Text-Based Person Search (TBPS) has seen significant progress with vision-language models (VLMs), yet it remains constrained by limited training data...
Keita Takeda, Tomoya Sakai
This study investigates the feature representations produced by publicly available open source medical vision-language models (VLMs). While medical...
Muhammad Khalifa, Lajanugen Logeswaran, Jaekyeom Kim +6 more
Large language models (LLMs) are increasingly used as judges to evaluate agent performance, particularly in non-verifiable settings where judgments...
Zhihao Chen, Zirui Gong, Jianting Ning +2 more
Federated Rank Learning (FRL) is a promising Federated Learning (FL) paradigm designed to be resilient against model poisoning attacks due to its...
Yijin Zhou, Xiaoya Lu, Dongrui Liu +2 more
The rapid advancement of Large Language Model (LLM)-based Multi-Agent Systems (MAS) has introduced significant security vulnerabilities, where...
Saswat Das, Ferdinando Fioretto
This work addresses the computational challenge of enforcing privacy for agentic Large Language Models (LLMs), where privacy is governed by the...
Adeyemi Adeseye, Aisvarya Adeseye
Loop vulnerabilities are one major risky construct in software development. They can easily lead to infinite loops or executions, exhaust resources,...
Víctor Mayoral-Vilches, Stefan Rass, Martin Pinzger +14 more
Cybersecurity superintelligence -- artificial intelligence exceeding the best human capability in both speed and strategic reasoning -- represents...
Daisuke Miyamoto, Takuji Iimura, Narushige Michishita
With the spread of generative AI in recent years, attacks known as Whaling have become a serious threat. Whaling is a form of social engineering that...
Linbo Cao, Lihao Sun, Yang Yue
Large Language Models (LLMs) are increasingly deployed as autonomous agents capable of actions with real-world impacts beyond text generation. While...
Haodong Chen, Ziheng Zhang, Jinghui Jiang +2 more
Cloud environments face frequent DDoS threats due to centralized resources and broad attack surfaces. Modern cloud-native DDoS attacks further evolve...
Andrew Crossman, Jonah Dodd, Viralam Ramamurthy Chaithanya Kumar +5 more
MITRE ATT&CK is a cybersecurity knowledge base that organizes threat actor and cyber-attack information into a set of tactics describing the reasons...
Luis Lazo, Hamed Jelodar, Roozbeh Razavi-Far
In this study, we propose a homotopy-inspired prompt obfuscation framework to enhance understanding of security and safety vulnerabilities in Large...
Mohammad Shamim Ahsan, Peng Liu
In the network security domain, due to practical issues -- including imbalanced data and heterogeneous legitimate network traffic -- adversarial...
Renmiao Chen, Yida Lu, Shiyao Cui +6 more
As Multimodal Large Language Models (MLLMs) acquire stronger reasoning capabilities to handle complex, multi-image instructions, this advancement may...
Hossein Naderi, Alireza Shojaei, Lifu Huang +3 more
Robots are expected to play a major role in the future construction industry but face challenges due to high costs and difficulty adapting to dynamic...
William Pan, Guiran Liu, Binrong Zhu +4 more
The rapid expansion of IoT deployments has intensified cybersecurity threats, notably Distributed Denial of Service (DDoS) attacks, characterized by...
Zhihao Dou, Dongfei Cui, Weida Wang +7 more
Split Learning (SL) offers a framework for collaborative model training that respects data privacy by allowing participants to share the same dataset...
Xiaochen Zhu, Mayuri Sridhar, Srinivas Devadas
Modern machine learning models are increasingly deployed behind APIs. This renders standard weight-privatization methods (e.g. DP-SGD) unnecessarily...
Yilin Tang, Yu Wang, Lanlan Qiu +4 more
Large language models (LLMs) have shown strong capabilities in multi-step decision-making, planning and actions, and are increasingly integrated into...
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 3,082+ 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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