CVE-2026-55765 (GCVE-0-2026-55765)
Vulnerability from cvelistv5 – Published: 2026-08-20 21:38 – Updated: 2026-08-25 15:29
VLAI
EPSS
VEX
Title
CloudNativePG: Cleartext role passwords recorded in pg_stat_statements allow privileged tenant roles to recover the PostgreSQL superuser credential and achieve RCE in the database pod
Summary
CloudNativePG is a platform designed to manage PostgreSQL databases within Kubernetes environments. Prior to 1.28.4 and 1.29.2, CloudNativePG embedded cleartext role passwords in `ALTER ROLE` and `CREATE ROLE` statements generated by SetUserPassword in pkg/management/postgres/utils/roles.go and appendPasswordOption in internal/management/controller/roles/postgres.go. When pg_stat_statements was preloaded with track_utility enabled and an untrusted tenant held pg_monitor or pg_read_all_stats, the tenant could recover platform-managed superuser or application-owner passwords, reconnect through enabled superuser TCP access, and execute operating system commands in the database pod with `COPY ... FROM PROGRAM`. Clusters using SCRAM-SHA-256 verifiers in managed-role Secrets were not affected. This issue is fixed in versions 1.28.4, 1.29.2, and 1.30.0.
Severity
8.5 (High)
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-25 15:29 UTC
Assigner
References
8 references
| URL | Tags |
|---|---|
| https://github.com/cloudnative-pg/cloudnative-pg/… | x_refsource_CONFIRM |
| https://github.com/cloudnative-pg/cloudnative-pg/… | x_refsource_MISC |
| https://github.com/cloudnative-pg/cloudnative-pg/… | x_refsource_MISC |
| https://github.com/cloudnative-pg/cloudnative-pg/… | x_refsource_MISC |
| https://github.com/cloudnative-pg/cloudnative-pg/… | x_refsource_MISC |
| https://github.com/cloudnative-pg/cloudnative-pg/… | x_refsource_MISC |
| https://github.com/cloudnative-pg/cloudnative-pg/… | x_refsource_MISC |
| https://github.com/cloudnative-pg/cloudnative-pg/… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| cloudnative-pg | cloudnative-pg |
Affected:
< 1.28.4
Affected: >= 1.29.0, < 1.29.2 |
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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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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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