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 121–140 of 735 papers
Clear filtersChuanchao Zang, Zijian Cao, Xiangtao Meng +6 more
Long-term memory is becoming a core capability of LLM agents, enabling personalization and long-horizon interaction. However, memory mechanisms that...
Hao Yan, Ziyu Yao
Large language models (LLMs) frequently generate source code containing vulnerabilities, yet little work studies the internal mechanisms that...
Yitian Zhou, Jingyu Zheng, Qiliang Jiang +6 more
Industrial control systems (ICSs) rely on programmable logic controllers (PLCs) to connect networked computation with physical control. Tool-using...
Ping Wang, Xiangguo Sun, Bingbing Xu +2 more
Large language models (LLMs) frequently produce confident yet factually incorrect responses when user inputs contain misleading premises, a...
Zhiyuan Xu, Muhammad Firhard Roslan, Joseph Gardiner +2 more
Safety evaluation is critical for assessing whether aligned Large Language Models (LLMs) remain robust against jailbreak attacks. Existing automated...
Longzhu He, Zelang Wen, Chaozhuo Li +1 more
Large language models (LLMs) have recently advanced graph neural networks (GNNs) by enriching node representations with semantic information, giving...
Jinpu Jiang, Xuan Wu, Wenhao Song +6 more
Retrieval-Augmented Generation (RAG) has emerged as a powerful architecture for Question Answering (QA) by integrating external information into...
Pei-Sze Tan, Tasuku Igarashi, Isao Echizen
Agentic AI assistants are increasingly used in everyday life. However, they may also be misused to support harmful manipulation in interpersonal...
Ze Sheng, Aleksandar Kezic, Zhicheng Chen +1 more
Evaluating the ability of large language models (LLMs) to discover software bugs is increasingly important. Existing benchmarks typically evaluate...
Lin-Fa Lee, YI-YU Chang, Kuo-Hui Yeh
The emerging W3C WebMCP proposal enables LLM agents to invoke tools exposed by web pages. In multi-party web environments, however, integrating agent...
Yueyang Quan, Anjun Gao, Yufei Xia +2 more
Retrieval-augmented generation (RAG) improves the factuality of large language models by grounding responses in external documents, but it also...
Trung-Anh Dang, Duy-Cuong Bui, Ngoc-Son Vu +2 more
Continual learning faces the persistent challenge of catastrophic forgetting, where sequential task updates degrade previously acquired knowledge....
Seokjin Hwang, Yuting, Li +1 more
Instance encoding is a popular empirical technique for privacy enhancement when sharing data to an untrusted server. It transforms sensitive data...
Seokjin Hwang, Yuting Li, Kiwan Maeng
Instance encoding is a popular empirical technique for privacy enhancement when sharing data to an untrusted server. It transforms sensitive data...
Lorenzo Bossi, Federico Saccani, Francesco Panebianco +4 more
Cyber threat intelligence from underground forums has traditionally relied on passive monitoring. However, as users have become more aware of...
Yiting Qu, Ziqing Yang, Chi Cui +3 more
Hidden chain-of-thought (CoT) traces, especially those from frontier proprietary large reasoning models (LRMs), are valuable model assets. Yet...
Yue Wang, Yi Liu, Gelei Deng +4 more
Agent Skills extend LLM agents with reusable instruction packages that may also include scripts, resources, and service configuration. This creates a...
Ye Tao, Hong Shen, Hui Tian +2 more
Gradient inversion attacks recover private training text from gradients shared in federated learning, posing a serious threat to collaborative model...
Muris Sladić, Veronica Valeros, Eman Alibalić +1 more
LLM-based SSH honeypots often use closed cloud LLMs because they give strong shell realism, but cloud models create deployment problems. These...
Yajing Bai, Jinhao Duan, Jie Peng +4 more
Large language models are increasingly deployed through agent harnesses that manage tools, extensions, persistent state, permissions, and external...
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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