CVE-2026-50200 (GCVE-0-2026-50200)
Vulnerability from cvelistv5 – Published: 2026-06-17 21:44 – Updated: 2026-06-18 15:45
VLAI
EPSS
VEX
Title
Steeltoe's env sanitizer misses connection strings — leaks embedded DB passwords
Summary
Steeltoe is an open source project that provides a collection of libraries that helps users build cloud-native applications. In Steeltoe.Management.Endpoint prior to version 4.2.0 and Steeltoe.Management.EndpointCore prior to version 3.4.0, the `Sanitizer` component in the Environment actuator redacts configuration values by matching the configuration key name against a suffix list. The default list (`password`, `secret`, `key`, `token`, `.*credentials.*`, `vcap_services`) does not cover the standard .NET pattern `ConnectionStrings:<name>` or Steeltoe Connectors' `Steeltoe:Client:<type>:Default:ConnectionString`. There is no value-based scrubbing, so full connection string values including embedded `Password=` and `user:pass@host` segments are returned verbatim in `/actuator/env` responses. Steeltoe.Management.Endpoint 4.2.0 and Steeltoe.Management.EndpointCore 3.4.0 patch the issue. If an immediate upgrade is not possible: On the standard path, remove `env` from the actuator exposure list; add `.*connectionstring.*` to `KeysToSanitize` as a defense-in-depth measure for both paths; and/or require authorization on actuator endpoints.
Severity
7.5 (High)
SSVC
Exploitation: none
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-06-18 15:44 UTC
CWE
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://github.com/SteeltoeOSS/security-advisorie… | x_refsource_CONFIRM |
| https://github.com/SteeltoeOSS/Steeltoe/commit/be… | x_refsource_MISC |
| https://github.com/SteeltoeOSS/Steeltoe/commit/e5… | x_refsource_MISC |
Impacted products
2 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| SteeltoeOSS | Steeltoe.Management.Endpoint |
Affected:
< 4.2.0
|
guessed | |
| SteeltoeOSS | Steeltoe.Management.EndpointCore |
Affected:
< 3.4.0
|
guessed |
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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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