AI Threat Alert indexes 4,319+ 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 21–40 of 107 papers
Clear filtersXiuyuan Qi, Yi Wang, Wenjie Wang
Preference alignment enables language models to better reflect human values, but the preference data contains sensitive information, especially when...
Varun Kohli, Lee Bing Cheng, Nur Hazim Ghazali +3 more
Effective source recovery is critical to security applications such as malware analysis, vulnerability assessment, and legacy maintenance. Large...
Rachel S. Y. Teo, Yutaro Yamada, Shashank Kotyan +2 more
The rapid growth of scientific publishing has strained peer review, particularly in machine learning, raising concerns about declining review quality...
Zhengyang Zhu, Liming Huang, Runmin Ji +8 more
Coding agent harnesses mediate tool use and authorize actions, yet their security mechanisms and runtime effects remain incompletely characterized....
Keegan Wang, Anantika Mannby
A language model may behave safely in a single-agent evaluation yet produce vulnerable code when its context suggests that it is part of a...
Leizhen Zhang, Sheng Chen
Reliable learning-based vulnerability detection requires high-quality labels, yet datasets built from vulnerability-fixing commits may label...
Md. Robiul Islam Niloy
Open source software (OSS) ecosystems face growing threats from sophisticated supply chain attacks including typosquatting, dependency confusion,...
Serhii Zabolotnii
Benchmarks, audits, and agent protocols describe performance, permissions, and repair, but not how observed evidence should change an agent's...
Jihun Han, Yejin Jang, Byung Il Kwak +1 more
LLM-based agents extend their capabilities through third-party skills that provide task-specific instructions, scripts, and tool-use procedures....
Zhuo Chen, Hao Zeng, Jiawei Liu +6 more
The rapid growth of submissions and reviewing workload has accelerated the use of large language models (LLMs) in peer review. Prior studies suggest...
Zonghua Gu, Zeyu Gao, Amin Saremi +1 more
Adversarial machine learning has focused mainly on integrity, but availability is an increasingly consequential complement. Latency attacks (also...
Bravish Ghosh
Autonomous coding agents read untrusted files, run shell commands and spawn sub-agents with little supervision, yet their record is usually an...
Mauro Conti, Lorenzo Perinello, Umberto Salviati
The mobility sector is undergoing a paradigm shift driven by advances in Generative Artificial Intelligence. With a global market valued at...
Heewon Baek, Alsharif Abuadbba, Kristen Moore +2 more
Agentic AI extends LLM security beyond generated content to persistent state, autonomous actions, tool use, and interactions with humans and other...
Mengxiao Wang, Nitesh Saxena
Autonomous large language model (LLM) agents are moving rapidly into high-stakes domains, yet existing agentic-AI security studies remain largely...
Alex Remedios, Simon Storf, Fabien Roger +1 more
To keep coding agents from going off the rails, production systems now review each proposed action with a blocking monitor that can reject it before...
Guosen Wu, Huizhen Huang, Guoxiong Long +2 more
Privacy evaluations of tool-using LLM agents often inspect a designated action, final response, or attacker report. These local proxies can miss...
Son Ho, Cédric Fournet, Jonathan Protzenko +7 more
We develop a new methodology for verifying cryptographic software. We target production code written in Rust for performance and system integration,...
Divyanshu Kumar, Rohith HN, Nitin Aravind Birur +2 more
AI agents increasingly act through tools and delegated authority, but general incident repositories rarely capture the mechanisms needed to compare...
Victoria Lovelace, Cameron Berryman, Yuhan You +3 more
Large language model (LLM) agents are increasingly applied to penetration testing, but we still know little about what they can do or how they fail....
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 4,319+ 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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