CVE-2026-53602 (GCVE-0-2026-53602)
Vulnerability from cvelistv5 – Published: 2026-09-04 19:40 – Updated: 2026-09-04 19:50
VLAI
EPSS
VEX
Title
nebula-mesh - Host revocation is not durable: blocked/offboarded hosts can regain a valid certificate
Summary
nebula-mesh is a self-hosted control plane for Slack Nebula mesh VPN. Prior to version 0.3.7, two related authorization gaps let a host that should no longer be trusted obtain a fresh, valid Nebula certificate, because nebula-mgmt does not re-evaluate revocation/authorization state at certificate issuance time — only at poll time. Firstly, the blocklist is not enforced at sign / re-enroll time. internal/api/enroll.go:128 calls caMgr.Sign(...) without consulting the blocklist. The blocklist is only checked in the poll path (internal/api/updates.go:57, fingerprintInBlocklist). The blocklist is keyed by certificate fingerprint (internal/store/sqlite.go), so a re-enrollment produces a new fingerprint that is not in the blocklist. Secondly, renewal does not re-validate operator / CA status. Auto-renewal at poll time (internal/api/updates.go:285-319, signHostCert) reads host.Name, host.Groups, host.NebulaIPs from the DB and re-signs without checking whether the owning operator is still active or the CA still valid. DisableOperator (internal/store/sqlite_operators.go) revokes sessions and API keys but does not retire the operator's CAs, and pki/signer.go checks only CA cert time-expiry, not operator/CA status. This issue has been patched in version 0.3.7.
Severity
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-04 19:49 UTC
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://github.com/forgekeep/nebula-mesh/security… | x_refsource_CONFIRM |
| https://github.com/forgekeep/nebula-mesh/issues/178 | x_refsource_MISC |
| https://github.com/forgekeep/nebula-mesh/releases… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| forgekeep | nebula-mesh |
Affected:
< 0.3.7
|
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
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.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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.
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.
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