  Products certified under security certification frameworks such as Common
Criteria undergo significant scrutiny during the costly certification process.
Yet, critical vulnerabilities, including private key recovery (ROCA, Minerva,
TPM-Fail...), get discovered in certified products with high assurance levels.
Furthermore, assessing which certified products are impacted by such
vulnerabilities is complicated due to the large amount of unstructured
certification-related data and unclear relationships between the certified
products. To address these problems, we conducted a large-scale automated
analysis of Common Criteria certificates. We trained unsupervised models to
learn which vulnerabilities from NIST's National Vulnerability Database impact
existing certified products and how certified products reference each other.
Our tooling automates the analysis of tens of thousands of
certification-related documents, extracting machine-readable features where
manual analysis is unattainable. Further, we identify the security requirements
that are associated with products being affected by fewer and less severe
vulnerabilities. This indicates which aspects of certification correlate with
higher security. We demonstrate how our tool can be used for better
vulnerability mitigation on four case studies of known, high-profile
vulnerabilities. All tools and continuously updated results are available at
https://seccerts.org
