CVE-2026-100708 (GCVE-0-2026-100708)
Vulnerability from cvelistv5 – Published: 2026-09-26 13:24 – Updated: 2026-09-28 14:49
VLAI
EPSS
VEX
Title
Froxlor before 2.3.13 Private Key Disclosure via Certificates API
Summary
Froxlor before 2.3.13 returns the ssl_key_file column — which stores the raw PEM TLS private-key content — verbatim in the JSON responses of the Certificates.get and Certificates.listing API commands, because the results of the underlying domain_ssl_settings queries are passed through ApiCommand::response() without any field stripping or allowlist. A low-privileged authenticated customer API caller can retrieve the private keys of their own domains' certificates, including Let's Encrypt keys that Froxlor generates server-side and stores root-only (0600) and to which the customer otherwise has no filesystem access; reseller and customers_see_all admin accounts can dump the private keys of other principals through the same sink. Disclosed keys enable domain impersonation, passive decryption of captured TLS traffic, and active machine-in-the-middle attacks.
Severity
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-28 14:49 UTC
CWE
- CWE-200 - Exposure of Sensitive Information to an Unauthorized Actor
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/froxlor/froxlor/security/advis… | vendor-advisory |
| https://www.vulncheck.com/advisories/froxlor-befo… | third-party-advisory |
Impacted products
Date Public
2026-09-06 00:00
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Sightings
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vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
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The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
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Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.