CVE-2026-18110 (GCVE-0-2026-18110)
Vulnerability from cvelistv5 – Published: 2026-09-15 16:33 – Updated: 2026-09-15 16:33
VLAI
EPSS
VEX
Title
Concrete CMS 9.0.0 through 9.5.2 is vulnerable to missing authorization in the user selector autocomplete endpoint (/ccm/system/user/autocomplete), allowing an unauthenticated attacker to retrieve the complete backend user directory — internal ID, username
Summary
Concrete CMS 9 (9.0.0 through 9.5.2) does not perform an authorization check on the user selector autocomplete endpoint (/ccm/system/user/autocomplete), which backs the "Preview as User" panel and other user-selector components. The endpoint validates only a CSRF-style access token that is bound to the selector's display options rather than to the caller's identity or permissions, and that token is issued to anonymous visitors because the selector renders without an authorization check. Because an empty query resolves to a match-all filter, an unauthenticated attacker can submit an empty search and paginate the results to enumerate every backend account, disclosing the internal user ID, username, and email address of all administrative users, including the super-administrator (user ID 1). No password hashes or session material are disclosed The Concrete CMS security team gave this vulnerability a CVSS v4.0 score of 8.7 with vector CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:N/VA:N/SC:N/SI:N/SA:N. Thanks thirtythree and YesWeHack for reporting.
Severity
CWE
- CWE-862 - Missing Authorization
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://documentation.concretecms.org/developers/… | release-notes |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Concrete CMS | Concrete CMS |
Affected:
9.0.0 , ≤ 9.5.2
(git)
|
guessed |
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"title": "Concrete CMS 9.0.0 through 9.5.2 is vulnerable to missing authorization in the user selector autocomplete endpoint (/ccm/system/user/autocomplete), allowing an unauthenticated attacker to retrieve the complete backend user directory \u2014 internal ID, username",
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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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