AI Threat Alert indexes 3,406+ 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 61–80 of 1,315 papers
Clear filtersQi Zhao, Christian Wressnegger
Recent training-time defenses against neural backdoors isolate a benign subset from poisoned training data, to learn a backdoor-free model from it....
Maria Rosaria Briglia, Igor Maljkovic, Antonio Emanuele Cinà +3 more
Vision--Language Models (VLMs) are increasingly deployed through a model supply chain in which pretrained checkpoints, architecture definitions, text...
Parmida Geranmayeh, Onur Günlü
In next-generation wireless networks, communication systems are expected to go beyond simple data transmission and simultaneously provide high data...
Yu Yan, Jiahao Chen, Siqi Lu +6 more
Large Language Models (LLMs) have been widely applied in high-stakes decision-making scenarios such as corporate strategy, and users are increasingly...
Zixuan Wu, Cristina Nita-Rotaru
Large language models are increasingly deployed for security-sensitive tasks such as vulnerability detection and code review. Their reliance on...
Yaroslav Popryho, Debjit Pal, Inna Partin-Vaisband
Modern System-on-Chip (SoCs) often contain hundreds of millions to tens of billions of gates, making existing Hardware Trojan (HT) detection methods...
Zhaoxi Zhang, Xiaomei Zhang
Long-lived AI agents increasingly evolve after deployment by retaining experience, acquiring skills and tools, revising workflows, delegating work,...
Nikolaos Kekatos, Stylianos Basagiannis, Panagiotis Katsaros +2 more
Swarms of LLM-assisted autonomous robots are increasingly proposed for cooperative intelligence, surveillance, and reconnaissance (ISR) in contested...
Junchi Liao, Jiawen Deng, Fuji Ren
Visible tests are a common gate for LLM-generated code, but passing them does not certify specification correctness. We study a deployment-like...
Zhaoqi Wang, Zijian Zhang, Xiaomei Yuan +4 more
Large language models increasingly use search tools to retrieve up-to-date information, introducing a new attack surface in which retrieved documents...
Andreas Happe, Jürgen Cito, Jasmin Wachter
LLM-driven autonomous agents are reshaping offensive security. Unlike traditional penetration-testing tooling -- deterministic, narrowly scoped, and...
Lynn Delcon, Andres Algaba, Vincent Ginis
Perturbation techniques that turn unsuccessful jailbreak prompts into successful ones are continuously evolving, constituting a major security threat...
Yichi Zhang, Zhiqi Wang, Huan Zhang +1 more
Key-Value (KV) cache reduces inference latency in large language models (LLMs). Traditional prefix-based reuse has low cache hit rates across...
Li Zeng, Zeyu Ye, Meng Xie +4 more
Vision-Language Models (VLMs) are known to be vulnerable to adversarial attacks, where subtle perturbations to images or texts induce erroneous...
Qinying Wang, Yong Yang, Yuan Chen +2 more
x402 is an emerging payment protocol for Web APIs and autonomous AI agents. x402 extends HTTP 402 with a payment negotiation flow and delegates...
Yohann Sidot
We study a five-agent CI/CD pipeline (triage -> developer -> security-scan -> review -> approve/deploy), built from five distinct production LLMs...
Sibo Wang, Jie Zhang, Shiguang Shan +2 more
While Large Vision-Language Models (LVLMs), represented by LLaVA and GPT-4V, have demonstrated remarkable capabilities, their visual inputs remain...
SangJin Park, Myungsub Choi, Jineok Kim +1 more
LLM-agent defenses are typically evaluated one session at a time. In deployment, however, attacks can be distributed across independent agents,...
Xinting Liao, Behnoosh Zamanlooy, Masoumeh Shafieinejad +4 more
Textual Collaborative Prompt Optimization (TCPO) extends Textgrad (Yuksekgonul et al., 2025) to a decentralized setting by allowing multiple clients...
Muxi Lyu, Karen Shieh, Yiwei Hou +3 more
Cross-Site Scripting (XSS) remains one of the most prevalent and damaging classes of web vulnerabilities. LLM-based coding agents offer a promising...
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 3,406+ 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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