CVE-2026-100612 (GCVE-0-2026-100612)
Vulnerability from cvelistv5 – Published: 2026-09-26 13:22 – Updated: 2026-09-26 13:22
VLAI
EPSS
VEX
Title
Capgo SSO Provider ID Authentication Bypass via Incomplete Migration
Summary
Capgo (capgo.app) through version 12.261.0 contains an incomplete access-control fix for the public.sso_providers table. Migration 20260826100000_sso_providers_block_direct_active_insert.sql installs a BEFORE UPDATE guard (enforce_sso_provider_client_update_guard()) that freezes only the dns_verified_at, domain, status and enforce_sso columns; provider_id (as well as metadata_url and attribute_mapping) is left writable. Because the table is granted ALL to the anon and authenticated roles with no column-level restriction, and PostgreSQL row-level security policies such as allow_org_admins_update_sso_providers constrain only which row may be updated and not which columns, a user holding the org_admin tier permission org.update_settings can PATCH provider_id over PostgREST to an identity provider under their control. Since provider_id is the trust anchor binding an email domain to an authorized IdP, the attacker can then authenticate through their own IdP while asserting the org owner's email; the server-side provider match succeeds and the merge routine attaches the attacker's SSO identity to the existing owner account, nulls its password, and deletes its other identities and sessions. This results in vertical privilege escalation from org_admin to org owner/super_admin, account takeover, and lockout of the legitimate owner. Exploitation requires that the target organization has an active SSO provider configured and that the attacker already holds org_admin in that organization. No patched version is available.
Severity
CWE
- CWE-639 - Authorization Bypass Through User-Controlled Key
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/Cap-go/capgo.app/security/advi… | vendor-advisory |
| https://www.vulncheck.com/advisories/capgo-sso-pr… | third-party-advisory |
Date Public
2026-09-08 00:00
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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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