Tootfinder

An Automated Attack Investigation Approach Leveraging Threat-Knowledge-Augmented Large Language Models
Rujie Dai, Peizhuo Lv, Yujiang Gui, Qiujian Lv, Yuanyuan Qiao, Yan Wang, Degang Sun, Weiqing Huang, Yingjiu Li, XiaoFeng Wang
https://arxiv.org/abs/2509.01271

An Automated Attack Investigation Approach Leveraging Threat-Knowledge-Augmented Large Language Models
Advanced Persistent Threats (APTs) are prolonged, stealthy intrusions by skilled adversaries that compromise high-value systems to steal data or disrupt operations. Reconstructing complete attack chains from massive, heterogeneous logs is essential for effective attack investigation, yet existing methods suffer from poor platform generality, limited generalization to evolving tactics, and an inability to produce analyst-ready reports. Large Language Models (LLMs) offer strong semantic understan…

Futurity as Infrastructure: A Techno-Philosophical Interpretation of the AI Lifecycle
Mark Cote, Susana Aires
https://arxiv.org/abs/2508.15680 https://arxi…

Futurity as Infrastructure: A Techno-Philosophical Interpretation of the AI Lifecycle
This paper argues that a techno-philosophical reading of the EU AI Act provides insight into the long-term dynamics of data in AI systems, specifically, how the lifecycle from ingestion to deployment generates recursive value chains that challenge existing frameworks for Responsible AI. We introduce a conceptual tool to frame the AI pipeline, spanning data, training regimes, architectures, feature stores, and transfer learning. Using cross-disciplinary methods, we develop a technically grounded…