Chain-Sawing: A Longitudinal Analysis of Certificate Chains
Bibliographic record
Abstract
The security and integrity of TLS certificates are essential for ensuring secure transmission over the Internet and protecting millions of people from man-in-the-middle attacks. Certificate Authorities (CAs) play a crucial role in issuing and managing these certificates. This paper presents a longitudinal analysis of certificate chains for popular domains, examining their evolution over time and across different categories. Using publicly available certificate data, primarily from crt.sh, we created a longitudinal dataset of certificate chains for domains from the Tranco top-1M list. After categorizing the certificates based on their type and service category, we analyze a selected set of domains over time and identify the patterns and trends that emerge in their certificate chains. Our analysis reveals several noteworthy trends, including a trend towards shorter certificate chains and fewer paths from domains to root certificates. This implies that the certificate process is becoming more simplified and streamlined. Combined with our observations that there is an increasing use of new CAs and a shift in the types of certificates used that we observe, we expect part of this to be an effect of individual choices made by some popular CAs (e.g., less cross-signings). In general, the observed trends, patterns, and findings capture tradeoffs in overhead, backward compatibility, and security. The quick shifts in some of the observed metrics (e.g., chain lengths) therefore also highlight the importance of continued monitoring and analysis of certificate chains.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".