Measuring and Characterizing Propagation of Reuse RSA Certificates and Keys Across PKI Ecosystem
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The insecurities of public-key infrastructure on the Internet have been the focus of research for over a decade. The extensive presence of broken, weak, and vulnerable cryptographic keys has been repeatedly emphasized by many studies. Analyzing the security implications of cryptographic keys’ vulnerabilities, several studies noted the presence of public key reuse. While the phenomenon of private key sharing was extensively studied, the prevalence of public key sharing on the Internet remains largely unknown. In this work, we perform a large-scale analysis of public key reuse within the PKI ecosystem. We investigate the presence and distribution of duplicate X.509 certificates and reused RSA public keys across a large collection containing over 314 million certificates and over 13 million SSH keys collected by different sources at different times. We analyze the cryptographic weaknesses of duplicate certificates and reused keys and investigate the reasons and sources of reuse. Our results reveal that certificate and key sharing are common and persistent. Our findings show over 10 million certificates and 17 million public keys are reused across time and shared between our collections. We observe keys with non-compliant cryptographic elements stay available for an extended period of time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it