CVE-2026-34215
Published: 31 March 2026
Summary
CVE-2026-34215 is a high-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Parseplatform Parse-Server. Its CVSS base score is 8.2 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Multi-Factor Authentication Interception (T1111); ranked at the 21.8th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-17604
Vulnerability details
Parse Server is an open source backend that can be deployed to any infrastructure that can run Node.js. Prior to versions 8.6.63 and 9.7.0-alpha.7, the verify password endpoint returns unsanitized authentication data, including MFA TOTP secrets, recovery codes, and OAuth…
more
access tokens. An attacker who knows a user's password can extract the MFA secret to generate valid MFA codes, defeating multi-factor authentication protection. This issue has been patched in versions 8.6.63 and 9.7.0-alpha.7.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Vuln directly exposes MFA secrets/OAuth tokens via API response (enables interception of MFA material and theft of app tokens/credentials).
Affected Assets
Mitigating Controls
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Automated marking applies security attributes to system outputs, making it harder for attackers to exploit unmarked sensitive information leading to unauthorized exposure.
Proper attribute retention and permitted-value enforcement limits unauthorized actors from accessing sensitive information lacking correct labels.
Prevents unauthorized exposure of sensitive information by prohibiting untrusted external systems from processing or storing it.
By enforcing authorization matching prior to sharing, the control reduces the risk of exposing sensitive information to unauthorized actors.
Review and removal of nonpublic information from publicly accessible systems directly prevents exposure of sensitive data to unauthorized actors.
Data mining protection mechanisms detect and block unauthorized bulk extraction of sensitive data, directly mitigating exposure to unauthorized actors.
Literacy training teaches users to recognize and avoid actions that result in unauthorized exposure of sensitive information.
Retaining and monitoring training records confirms personnel have completed privacy and security awareness training on handling sensitive data, reducing the chance of unauthorized exposure due to lack of knowledge.