AI Threat Alert indexes 3,371+ 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 450 papers
Clear filtersRoberto Riaño, Gorka Abad, Stjepan Picek +1 more
Backdoor attacks on Spiking Neural Networks (SNNs) have primarily assumed dirty-label poisoning, in which triggered training samples are relabeled to...
Nguyen Duc Thai, Junhao Dong, Sua Qi Rong +2 more
Vision-language models (VLMs), such as CLIP, are vulnerable to adversarial attacks, posing a serious problem for real-life applications and...
Xuanze Chen, Xukang Xie, Wentao Fu +3 more
Memory systems allow agents to retain and reuse information from past interactions, but they can also let malicious content persist. A malicious...
Ruoyu Wang, Heng Zhao, Renjie Wu +4 more
Large language model (LLM) agents automate penetration testing through an observation-action loop, selecting actions based on observations returned...
Ads Dawson, Adrian Wood
Stealth, the discipline of achieving an objective without revealing your presence, capabilities, or collected intelligence, is what separates...
Pau Baguer, J. Xavier Salvat Lozano, Gines Garcia-Aviles +1 more
The shift to O-RAN architectures marks a turning point in cellular security, where increased openness and modularity directly translate into a...
Qi 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...
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...
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...
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...
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...
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...
Chengheng Li-Chen, Kyuhee Kim
Regulatory regimes such as the EU AI Act mandate machine-readable marking of synthetic text, but existing watermark detectors rely on the generating...
Tanveer Ahmed, Seyedali Pourmoafil
Phishing emails remain one of the most persistent cybersecurity threats, and machine-learning classifiers are widely used to detect them. Most...
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 3,371+ 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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