Matthew Grofsky | Searcharxiv

arXiv2025

Freshness and the Limits of Heuristic Trend Detection in Temporal RAG

We present a lightweight, model-agnostic temporal layer for RAG and use cybersecurity data to separate two problems that are usually conflated. For freshness, a half-life recency prior surfaces the newest relevant item where a cosine-only baseline scores 0.00; on a hard NVD CVE test, where the freshest item is not the most similar, it reaches Latest@10 of 0.60 versus 0.20 for a semantic-then-newest baseline, but stays partial and parameter-sensitive. For topic evolution, a heuristic tracker's low 0.08 macro-F1 is driven by the labeling rule, not the clusterer (HDBSCAN: 0.10; fixing the rule alone reaches 0.49, and 0.96 without clustering noise). We contribute a reproducible decoupling of the two, with honest real-data scope and a reference implementation.

cs.LG↗

arXiv2025

AegisShield: Democratizing Cyber Threat Modeling with Generative AI

The increasing sophistication of technology systems makes traditional threat modeling hard to scale, especially for small organizations with limited resources. This paper develops and evaluates AegisShield, a generative AI enhanced threat modeling tool that implements STRIDE and MITRE ATT&CK to automate threat generation and provide systematic assessments. By integrating real time threat intelligence from the National Vulnerability Database and AlienVault Open Threat Exchange, AegisShield produces streamlined and accessible threat descriptions. Our assessment of 243 threats from 15 case studies and over 8000 AI generated threats shows that AegisShield reduces complexity (p less than 0.001), yields outputs semantically aligned with expert developed threats (p less than 0.05), and achieves an 85.4 percent success rate in mapping threats to MITRE ATT&CK techniques (p less than 0.001). Automating and standardizing threat modeling helps under resourced organizations address risk earlier and supports wider adoption of secure by design practices.

cs.CR↗