AI Threat Alert indexes 3,397+ 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 341–360 of 458 papers
Clear filtersLi Lin, Siyuan Xin, Yang Cao +1 more
Watermarking large language models (LLMs) is vital for preventing their misuse, including the fabrication of fake news, plagiarism, and spam. It is...
Hua Ma, Ruoxi Sun, Minhui Xue +4 more
Accurate time-series forecasting is increasingly critical for planning and operations in low-carbon power systems. Emerging time-series large...
Jamal Al-Karaki, Muhammad Al-Zafar Khan, Rand Derar Mohammad Al Athamneh
The scarcity of cyberattack data hinders the development of robust intrusion detection systems. This paper introduces PHANTOM, a novel adversarial...
Neha, Tarunpreet Bhatia
Intrusion Detection Systems (IDS) are critical components in safeguarding 5G/6G networks from both internal and external cyber threats. While...
Miranda Christ, Noah Golowich, Sam Gunn +2 more
Watermarks are an essential tool for identifying AI-generated content. Recently, Christ and Gunn (CRYPTO '24) introduced pseudorandom...
Botao 'Amber' Hu, Bangdao Chen
The emerging "agentic web" envisions large populations of autonomous agents coordinating, transacting, and delegating across open networks. Yet many...
George Mikros
Large language models (LLMs) present a dual challenge for forensic linguistics. They serve as powerful analytical tools enabling scalable corpus...
Sima Jafarikhah, Daniel Thompson, Eva Deans +2 more
Manual vulnerability scoring, such as assigning Common Vulnerability Scoring System (CVSS) scores, is a resource-intensive process that is often...
Donghang Duan, Xu Zheng, Yuefeng He +3 more
Current LLM-based text anonymization frameworks usually rely on remote API services from powerful LLMs, which creates an inherent privacy paradox:...
Jinbo Liu, Defu Cao, Yifei Wei +6 more
Graph topology is a fundamental determinant of memory leakage in multi-agent LLM systems, yet its effects remain poorly quantified. We introduce MAMA...
Itay Yona, Amir Sarid, Michael Karasik +1 more
We introduce $\textbf{Doublespeak}$, a simple in-context representation hijacking attack against large language models (LLMs). The attack works by...
Hanxiu Zhang, Yue Zheng
The protection of Intellectual Property (IP) in Large Language Models (LLMs) represents a critical challenge in contemporary AI research. While...
Thomas Rivasseau
Current research on operator control of Large Language Models improves model robustness against adversarial attacks and misbehavior by training on...
Adel Chehade, Edoardo Ragusa, Paolo Gastaldo +1 more
Traffic classification (TC) plays a critical role in cybersecurity, particularly in IoT and embedded contexts, where inspection must often occur...
Zixia Wang, Gaojie Jin, Jia Hu +1 more
Recent advancements in Large Language Models (LLMs) have led to their widespread adoption in daily applications. Despite their impressive...
Alexander Boyd, Franz Nowak, David Hyland +2 more
World models have been recently proposed as sandbox environments in which AI agents can be trained and evaluated before deployment. Although...
Aaron Sandoval, Cody Rushing
The field of AI Control seeks to develop robust control protocols, deployment safeguards for untrusted AI which may be intentionally subversive....
Adeela Bashir, The Anh han, Zia Ush Shamszaman
The integration of large language models (LLMs) into healthcare IoT systems promises faster decisions and improved medical support. LLMs are also...
K. J. Kevin Feng, Tae Soo Kim, Rock Yuren Pang +3 more
AI agents that take actions in their environment autonomously over extended time horizons require robust governance interventions to curb their...
Tong Wu, Weibin Wu, Zibin Zheng
Equipped with various tools and knowledge, GPTs, one kind of customized AI agents based on OpenAI's large language models, have illustrated great...
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 3,397+ 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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