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 181–200 of 1,742 papers
Clear filtersMd Abu Yusuf, Oyshi Tabassum Aditi, Md Faysal Hosain Mazed
Large language model (LLM) agents are increasingly wired to consequential tools that send public alerts, dispatch responders, and query citizen...
Xiarun Chen, Jiaxing Xu, Bozhi Wu +6 more
Missing project context can prevent build-dependent analyzers from constructing program relations required for vulnerability detection, while neural...
Puyuan Yang, Shaoxuan Dong, Liujie Wu +2 more
The stance expressed in responses from retrieval-augmented generation systems is significantly influenced by the stance distribution of the retrieved...
Mingrui Liu, Liang He, Xinchu Wang +4 more
Broken access control (BAC) ranks among the most severe web-application vulnerability classes, yet its automated repair remains largely unexplored....
Chieh-Yuan Chang, Guan-Wei Chen, Chia-Yi Hsu +3 more
AI coding agents routinely clone repositories, install packages, and read source code obtained from the Web, so publicly hosted code becomes part of...
Pellegrino Casoria, Simon Pietro Romano
In the post-API landscape, social-media profiling has become inexpensive and automated: Large Language Models (LLMs) can extract structured...
Justin Tan, Miao Li, Ding Zhao
Computer-use agents increasingly handle private information and act on users' behalf across desktop and web applications, including email. Phishing...
Chao Gu, Quanyan Zhu
This paper studies the problem of safe tool execution for large language model (LLM) agents under prompt injection. Using Petri nets, we formalize...
Jinwei Gan, Zifeng Cheng, Zhiwei Jiang +3 more
Diffusion large language models (dLLMs) enable flexible masked generation through bidirectional context modeling, but this capability also introduces...
Roberto Passaro, Ariel Flint, Romualdo Pastor-Satorras +2 more
As large language models (LLMs) are deployed in interacting populations, their collective behaviour depends not only on the models but also on who...
Mingjie Li, Yukun Jiang, Yicong Tan +1 more
As AI generated images are getting popular these days, discussions on such generations are widely existed in current public forums, software...
Yuannuo Feng, Yubing Ye, Wenshuai Yao +4 more
Diffusion language models generate multiple token positions in parallel, but few-step decoding makes parallel commitment difficult: tokens that...
Yifan Wang, Dongliang Xu, Xiaoyong Li +3 more
Large language models (LLMs) have shown strong potential for cybersecurity, leading to growing interest in LLM-based automated cybersecurity log...
Wonjune Seo, Joonhyuk Baek, Yeseong Jung +1 more
Despite recent advances, deepfake detectors remain vulnerable to adversarial examples, particularly in diverse, real-world settings. We propose...
Mrityunjay Shukla, Shubham Roy, Biswabandan Panda
Large language models (LLMs) achieve high inference efficiency using optimized GPU kernels generated by just-in-time (JIT) compilation frameworks...
Farnoosh Hashemi, Shiyu Ji, Joice Chen +1 more
Large language models (LLMs) increasingly operate in multi-agent systems where they collaborate by responding to others' choices, creating endogenous...
Abhishek Mishra, Sagnik Chatterjee, Suyash Maniyar
Large language models have been reported to encode the harmfulness of a request separately from their decision to refuse it, and latent safety...
Ke Zhu, Yanli Chen, Ling Xiong +2 more
With the rapid adoption of large language models (LLMs) in text generation applications, it has become increasingly difficult to trace generated...
Edward C. Cheng, Jeshua Cheng, Alice Siu
AI agents capable of autonomous decision-making introduce significant challenges related to safety, alignment, accountability, and human oversight....
Michele Guerra, Wuyeh Jobe, Sanjana Nambiar +2 more
Web-searching Large Language Models (LLMs) have changed how users seek online information: instead of sifting through ranked lists of links, users...
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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