CVE-2026-66770

The CVSS 'low impact' rating for this DDL injection vulnerability fundamentally mischaracterizes the actual risk. Unlike data manipulation (INSERT/UPDATE/DELETE), DDL operations (CREATE/ALTER/DROP) modify the database schema itself — and these structural changes persist after the vulnerability is patched. A DROP TABLE statement doesn't leave a recoverable log entry; it removes data structures that may have taken years to build. If an attacker uses this to add privileged columns, insert persistence triggers, or corrupt referential constraints, those changes live in the schema layer independent of the vulnerable code. Your patch deployment checklist will show the vulnerability as 'resolved' while the structural compromise continues operating in production. The 'authenticated attacker' framing compounds the problem. SAP service accounts routinely hold database-level privileges that include DDL execution — the authentication requirement isn't the safeguard CVSS assumes. When phishing campaigns target SAP basis administrators (a documented recurring threat), that requirement evaporates entirely. Prioritize these actions: First, assume structural compromise is possible and audit your database schema against a known-good baseline immediately — look for unauthorized columns, triggers on core tables, and modified constraints. Second, extend your incident response timeline: standard remediation windows (60-90 days) create accumulating damage because each day of unpatched exposure is a day DDL modifications persist after the eventual patch. Third, treat the disclosure's vague language ('malicious changes to database structure') as a warning sign rather than reassurance — the absence of specific DDL operation details means you must verify which statements are actually possible, not assume they're limited. The core insight: DDL injection damage is non-recoverable through patching alone. Your vulnerability scanner will report the system as resolved while the structural modifications you didn't detect continue operating.

Reviewed through automated stages and approved by a human before publication.