CVE-2026-104803 (GCVE-0-2026-104803)
Vulnerability from cvelistv5 – Published: 2026-10-10 07:41 – Updated: 2026-10-10 07:41
VLAI
EPSS
VEX
Title
WPCOM Member <= 1.7.27 - Unauthenticated Authentication Bypass via 'uuid' and 'code' Parameters on Social-Login Callback
Summary
The WPCOM Member plugin for WordPress is vulnerable to Authentication Bypass in all versions up to, and including, 1.7.27 via the `uuid` and `code` parameters of the social-login callback handler registered on the `init` hook. The vulnerability exists because the `login` function's social-login flow performs no nonce validation, no OAuth state verification, and no per-visitor namespace isolation in the session store, allowing an unauthenticated attacker to issue a crafted GET request that writes an attacker-named, attacker-valued entry into the global session namespace (bypassing the per-visitor prefix by prepending an underscore), then issue a second GET request triggering `weapp_new_user()` to read that forged entry and resolve the attacker-supplied `openid` value to a bound WordPress account before `wp_set_auth_cookie()` establishes a fully authenticated session. This makes it possible for unauthenticated attackers to log in as any WordPress user — including administrators — whose bound social provider identifier (openid/unionid) is known or discoverable. Successful exploitation requires that the target site has at least one social provider configured (which activates the vulnerable handler) and that the attacker knows or can enumerate the victim account's bound openid or unionid.
Severity
9.8 (Critical)
CWE
- CWE-287 - Improper Authentication
Assigner
References
6 references
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| whyun | WPCOM Member |
Affected:
0 , ≤ 1.7.27
(semver)
|
guessed |
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"value": "Vendor Notified"
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"time": "2026-10-09T19:08:23.000Z",
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"title": "WPCOM Member \u003c= 1.7.27 - Unauthenticated Authentication Bypass via \u0027uuid\u0027 and \u0027code\u0027 Parameters on Social-Login Callback"
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}
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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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