# Qubes OS Security in the Public Record

arXiv: https://arxiv.org/abs/2607.14587 · HTML: https://mlsanity.com/paper/2607.14587/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 16 Jul 2026.

## Builds on (most cited first, 6 of 6)

- [A Systematic Evaluation of Transient Execution Attacks and Defenses](https://mlsanity.com/paper/1811.05441/) (2018)
- [Real time Detection of Spectre and Meltdown Attacks Using Machine Learning](https://mlsanity.com/paper/2006.01442/) (2020)
- [A Systematically Empirical Evaluation of Vulnerability Discovery Models: a Study on Browsers' Vulnerabilities](https://mlsanity.com/paper/1306.2476/) (2013)
- [MeltdownPrime and SpectrePrime: Automatically-Synthesized Attacks Exploiting Invalidation-Based Coherence Protocols](https://mlsanity.com/paper/1802.03802/) (2018)
- [Goldilocks Isolation: High Performance VMs with Edera](https://mlsanity.com/paper/2501.04580/) (2025)
- [Pre-cache: A Microarchitectural Solution to prevent Meltdown and Spectre](https://mlsanity.com/paper/2511.17726/) (2025)

## Abstract (arXiv)

Qubes OS is a revealing case for security measurement because its architecture makes component boundaries security-relevant. We present a protocol-driven longitudinal analysis of 109 public Qubes Security Bulletins (QSBs, 2011--2025), the official Qubes-maintained Xen Security Advisory (XSA) tracker, and a secondary vulnerability-event sensitivity series. The study measures the public advisory record rather than latent vulnerability incidence or realized compromise. The methodology combines audited deterministic component attribution, change-point analysis, overdispersion checks, severity-proxy weighting, censoring sensitivity, documentary latency lower bounds, and baseline-aware evaluation of vulnerability discovery models (VDMs). The results show persistent upstream dependence in that public record. On the official tracker, 113 of 464 XSAs affect Qubes; under primary labeling, 87 of 109 QSBs (79.8%) are attributable to Xen, CPU/microarchitectural, or other upstream components rather than Qubes-core logic, with similar results under weighted views. Change-point analyses identify 2015Q1 as the dominant break in the quarterly advisory series, while post-2018 annual disclosure rates are statistically flat. Poisson inferences are stable under dispersion diagnostics and negative-binomial sensitivity checks. The attribution codebook performs well in a stratified 30-QSB audit, and S-shaped VDMs fit descriptively but do not significantly outperform a rolling-mean baseline in short-horizon forecasts. Overall, the Qubes public advisory record appears stable, but not quiet: disclosure activity plateaus at a higher level than in the earliest years, while the observed burden remains concentrated in upstream trust anchors.