AI Threat Alert indexes 3,082+ 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 1061–1080 of 3,082 papers
Yuntong Zhang, Sungmin Kang, Ruijie Meng +2 more
Agentic AI has been a topic of great interest recently. A Large Language Model (LLM) agent involves one or more LLMs in the back-end. In the front...
Saikat Maiti
Autonomous AI agents powered by large language models are being deployed in production with capabilities including shell execution, file system...
Zichen Tang, Zirui Zhang, Qian Wang +3 more
Current Large Language Models (LLMs) are gradually exploited in practically valuable agentic workflows such as Deep Research, E-commerce...
Zichen Tang, Zirui Zhang, Qian Wang +3 more
Current Large Language Models (LLMs) are gradually exploited in practically valuable agentic workflows such as Deep Research, E-commerce...
Zhihua Wei, Qiang Li, Jian Ruan +4 more
Large vision-language models (VLMs) often exhibit weakened safety alignment with the integration of the visual modality. Even when text prompts...
Chengwei Wei, Jung-jae Kim, Longyin Zhang +2 more
Large Language Models (LLMs) with extended reasoning capabilities often generate verbose and redundant reasoning traces, incurring unnecessary...
Yi Ting Shen, Kentaroh Toyoda, Alex Leung
The Model Context Protocol (MCP) introduces a structurally distinct attack surface that existing threat frameworks, designed for traditional software...
Abhijeet Sahu, Shuva Paul, Richard Macwan
Cyber deception assists in increasing the attacker's budget in reconnaissance or any early phases of threat intrusions. In the past, numerous methods...
Hammad Atta, Ken Huang, Kyriakos Rock Lambros +11 more
Agentic LLM systems equipped with persistent memory, RAG pipelines, and external tool connectors face a class of attacks - Logic-layer Prompt Control...
Patrick Levi
Retrieval augmented generation systems have become an integral part of everyday life. Whether in internet search engines, email systems, or service...
Shenao Yan, Shimaa Ahmed, Shan Jin +4 more
Code generation large language models (LLMs) are increasingly integrated into modern software development workflows. Recent work has shown that these...
Taiwo Onitiju, Iman Vakilinia
Large Language Models increasingly power critical infrastructure from healthcare to finance, yet their vulnerability to adversarial manipulation...
Kushankur Ghosh, Mehar Klair, Kian Kyars +2 more
Provenance graphs model causal system-level interactions from logs, enabling anomaly detectors to learn normal behavior and detect deviations as...
Min Zeng, Shuang Zhou, Zaifu Zhan +1 more
Medical language models must be updated as evidence and terminology evolve, yet sequential updating can trigger catastrophic forgetting. Although...
Caglar Yildirim
Large language models (LLMs) are increasingly deployed as tool-using agents, shifting safety concerns from harmful text generation to harmful task...
Yong Zou, Haoran Li, Fanxiao Li +5 more
Recent progress in image generation models (IGMs) enables high-fidelity content creation but also amplifies risks, including the reproduction of...
Guangsheng Zhang, Huan Tian, Leo Zhang +4 more
Semantic segmentation models are widely deployed in safety-critical applications such as autonomous driving, yet their vulnerability to backdoor...
Deng Liu, Song Chen
Hardware faults, specifically bit-flips in quantized weights, pose a severe reliability threat to Large Language Models (LLMs), often triggering...
Gengxin Sun, Ruihao Yu, Liangyi Yin +3 more
Ensuring robust and fair interview assessment remains a key challenge in AI-driven evaluation. This paper presents CoMAI, a general-purpose...
Xiaobing Sun, Perry Lam, Shaohua Li +4 more
Modern LLMs employ safety mechanisms that extend beyond surface-level input filtering to latent semantic representations and generation-time...
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 3,082+ 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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