CVE-2026-33429
Parseplatform Parse-Server ≤ 8.6.54
Raw vector
CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:N/VC:L/VI:N/VA:N/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:XSummary
CVE-2026-33429 is a medium-severity Observable Discrepancy (CWE-203) vulnerability in Parseplatform Parse-Server. Its CVSS base score is 6.3 (Medium).
Operationally, ranked at the 24th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
The strongest mitigations our analysis identified map to AC-4 (Information Flow Enforcement) and AC-3 (Access Enforcement) — see the control section below for these in your framework.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-14970
Vulnerability Data
Parse Server is an open source backend that can be deployed to any infrastructure that can run Node.js. Prior to versions 8.6.54 and 9.6.0-alpha.43, an attacker can subscribe to LiveQuery with a watch parameter targeting a protected field. Although the…
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protected field value is properly stripped from event payloads, the presence or absence of update events reveals whether the protected field changed, creating a binary oracle. For boolean protected fields, the timing of change events is equivalent to knowing the field value. This issue has been patched in versions 8.6.54 and 9.6.0-alpha.43.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Insufficient information to map techniques.CVEs Like This One
Affected Assets
Mitigating Controls
Control response
Mitigating Controls (NIST 800-53 r5) AI
Directly blocks the unauthorized information flow that occurs when LiveQuery event presence/absence leaks protected-field state to an unauthorized subscriber.
Enforces access-control decisions on LiveQuery watch parameters so that protected fields cannot be observed even indirectly.
Enables monitoring and alerting on LiveQuery subscriptions that target fields marked as protected, surfacing the attempted oracle attack.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Secure SDLC practices directly prevent observable response discrepancies via consistent error handling and timing.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Accurate, synchronized timestamps reduce observable timing discrepancies that an attacker could exploit to infer sensitive information or distinguish between success and failure paths.