WPML Plugin Authentication Bypass via Race Condition

The WPML Multilingual CMS plugin for WordPress is vulnerable to Authentication Bypass via Race Condition in versions up to, and including, 4.9.7. This is due to the `process_iframe_request` function's language-per-domain SSO handler generating the cross-domain sign-in token via `wp_create_nonce('wpml_sso')` on the logout landing page, where the browser has already discarded its authentication cookies — so `wp_get_session_token()` returns an empty string and the token collapses to the shared anonymous nonce for the current 12-hour tick rather than a user-bound secret — and then storing it in the `wpml_sso_user_<token>_<sha256('wpml_sso'.$domain)>` transient mapped to the victim's `user_id` with no binding to the specific sign-out action or requesting client. Because an unauthenticated attacker can independently derive the identical token by triggering the logout confirmation flow on `wp-login.php?action=logout`, and because the `is_iframe_request()` gate conditions (the per-domain SSO `

Casky was already ahead

This CVE exploits attack patterns that Casky's 283matched skills already investigate — long before this vulnerability was disclosed. Claude's reasoning model maps these techniques to MITRE ATT&CK, so practitioners who ran these skills have already seen the threat behaviour in their findings.

Analysis

CVE-2026-76877 exploits a critical race condition in the WPML Multilingual CMS plugin's cross-domain single sign-on (SSO) handler. The vulnerability exists in the `process_iframe_request` function, which generates authentication tokens on the logout landing page after browsers have already discarded session cookies. This timing gap causes `wp_get_session_token()` to return an empty string, collapsing the nonce to a predictable shared value that attackers can forge. With a CVSS score of 8.1, this affects WordPress installations using WPML up to version 4.9.7, potentially exposing millions of multilingual sites to unauthorized account access and lateral movement.

Casky's 283 mapped skills leverage Claude's extended reasoning to detect the attack patterns underlying this vulnerability across MITRE ATT&CK's initial access (TA0004) and credential access (TA0006) tactics. Practitioners using Casky would identify suspicious indicators including: abnormal nonce generation patterns that don't correlate with active sessions, cross-domain authentication requests originating from logout pages, and successful login events preceded by missing or empty session tokens. The platform's skill set enables detection of race condition exploitation attempts through timing analysis of authentication flows, identification of forged SSO tokens, and anomalous account access from unexpected origins—surfacing these CWE-287 authentication bypass patterns before attackers can establish persistent access.

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GreyNoise Signal

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283 Casky skills cover this attack pattern

These skills use Claude AI's reasoning model to surface findings in the same attack categories as CVE-2026-76877.

abusing-dpapi-for-credential-access

red teaming · high

Run

abusing-shadow-credentials-for-privesc

red teaming · high

Run

Access with Stolen Session Cookie

identity access management · low

Run

MITRE ATT&CK Techniques

TA0004 TA0006

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