CVE-2026-63030
Wordpress 6.9 – 6.9.5
Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2026-63030 is a critical-severity Interpretation Conflict (CWE-436) vulnerability in Wordpress Wordpress. Its CVSS base score is 9.8 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 0.1% of CVEs by exploit likelihood; CISA has added it to the Known Exploited Vulnerabilities catalog.
The strongest mitigations our analysis identified map to SI-10 (Information Input Validation) and SI-2 (Flaw Remediation) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-45280
Vulnerability Data
WordPress 6.9.x before 6.9.5 and 7.0.x before 7.0.2 is affected by a REST API batch endpoint route confusion issue which, combined with the author__not_in WP_Query SQL Injection (CVE-2026-60137), could allow an attacker to perform SQL Injection and achieve Remote Code…
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Execution.
- CWE(s)
- KEV Date Added
- 21 July 2026
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
REST API route confusion enables remote exploitation of public-facing WordPress for SQLi leading to RCE.
Likely ATT&CK TechniquesAI
Techniques this vulnerability likely enables, inferred from its description, weakness type, and attributed-actor tradecraft. Confidence is per-technique.
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
Mitigating Controls (NIST 800-53 r5) AI
Directly requires timely application of security-relevant patches, eliminating the REST batch endpoint route confusion and linked SQLi flaw by upgrading to fixed WordPress versions.
Mandates validation of all inputs to the REST API batch endpoint, blocking the crafted author__not_in parameters that trigger the SQL injection.
Enforces access-control decisions on the WordPress REST API, limiting which users or unauthenticated callers can reach the vulnerable batch route.
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 reduce the chance of introducing parser or state-machine inconsistencies.
Correlating logs from multiple products can surface discrepancies caused by interpretation conflicts.
Runtime monitoring of software behavior can detect adverse outcomes stemming from differing interpretations.
Supplier risk assessments can identify products whose differing interpretations create systemic exposure.
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.
Security testing can detect and correct cases where one component misinterprets another’s state or messages.
Secure development lifecycle can require consistent interface contracts and canonicalization rules that reduce interpretation conflicts between components.
Explicit application security requirements can mandate unambiguous protocol and data-format specifications that prevent divergent interpretations.
Secure architecture principles include well-defined component boundaries and shared data models that limit conflicting state perceptions.
Secure coding standards can enforce canonical input handling and strict protocol compliance to avoid misinterpretation between products.