AI Threat Alert indexes 3,092+ 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 421–440 of 3,092 papers
Aditya Sridhar
Concept Bottleneck Models (CBMs) have emerged as a cornerstone approach for interpretable machine learning, providing human-understandable...
Dongpeng Zhang, Ke Ma, Yangbangyan Jiang +4 more
Adversarial images pose a severe security threat to multimodal large language models through prompt injection. Existing defenses largely lack a...
Wenjuan Li, Yitao Liu, Runze Chen +1 more
Background: Fine-tuning is central to adapting pre-trained Large Language Models (LLMs) to downstream tasks, but its reliance on training data,...
Esra Yeniaras
Quantum machine learning (QML) is moving from research prototypes to deployed cloud services. As QML enters regulated industries, the integrity of...
Haobo Zhang, Xutao Mao, Guangyuan Dong +5 more
Memory-backed agents need provenance that can survive leaked or migrated snapshots, where logs, visible outputs, and trusted metadata may be absent....
William Guanting Li, Alsharif Abuadbba, Kristen Moore +1 more
Penetration testing is essential to securing modern web infrastructures, yet traditional manual methods struggle to keep pace with their scale and...
Mahavir Dabas, Jihyun Jeong, Ming Jin +1 more
Modern LLM agents combine long-term memory for personalization with tool-calling interfaces for taking actions in the world -- a combination...
Ting Liu
Skills are increasingly used to package agent instructions, workflows, scripts, and reference materials. In enterprise settings, however, skills...
Tianyun Zhang, Zhen Yang, Haozhao Wang +2 more
Federated learning faces increasing threats from model poisoning attacks, which harms its application to improve privacy. Existing defense methods...
Huijun Zhou, Xiaohan Zhang, Haozhe Zhang +3 more
The Model Context Protocol (MCP) is emerging as a common interface connecting large language models (LLMs) with external services. Remote deployments...
Jianan Ma, Xiaohu Du, Ruixiao Lin +8 more
As autonomous agents (e.g., OpenClaw) increasingly operate with deep system-level privileges to execute complex tasks, they introduce severe,...
Xiang Chen, Yuxian Dong, Chao Li +6 more
Vision-language models (VLMs) have achieved strong performance across diverse multimodal tasks, but their adversarial robustness in visible-infrared...
Jingyi Kang, Junyu Lu, Bo Xu +4 more
Large language models (LLMs) require robust toxicity evaluation beyond explicit wording. This setting remains underexplored in Chinese, where...
Chengyan Ma, Jieke Shi, Ruidong Han +4 more
Trusted Execution Environments (TEEs) provide hardware-based isolation to protect sensitive data and computations from potentially compromised...
Chengyan Ma, Jieke Shi, Ruidong Han +3 more
Trusted Execution Environments (TEEs) provide hardware-enforced isolation that protects sensitive code and data from untrusted software. Despite...
Ziyuan Chen, Yueming Lyu, Yi Liu +4 more
While RAG systems are increasingly deployed in dynamic web search, temporal volatility amplifies their vulnerability to adversarial attacks. Existing...
Zhi Chen, Shehab Sarar Ahmed, Chenkai Wang +2 more
Congestion controllers (CCs) are critical to network performance, and yet their robustness under adverse conditions remains insufficiently...
Andy Han, Kristina Fujimoto, Avidan Shah +5 more
Aligned models can misbehave in several ways: they are often sycophantic, fall victim to jailbreaks, or fail to include appropriate safety warnings....
Aman Saxena, Jan Schuchardt, Yan Scholten +1 more
Randomized smoothing is a powerful tool for certifying robustness to adversarial perturbations, including poisoning attacks via randomized training...
Ze Sheng, Zhicheng Chen, Qingxiao Xu +2 more
Software vulnerabilities pose critical security threats, with nearly 50,000 CVEs reported in 2025. While Large Language Models (LLMs) show promise...
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 3,092+ 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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