CVE-2026-42316
Raw vector
CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:NCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2026-42316 is a medium-severity Improper Neutralization of Special Elements in Data Query Logic (CWE-943) vulnerability. Its CVSS base score is 6.5 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 27th 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 SI-10 (Information Input Validation) — see the control section below for these in your framework.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-29124
Vulnerability Data
kafka-sink-azure-kusto Kafka Connect plugin is the official Microsoft sink for Azure Data Explorer (Kusto). Prior to 5.2.3, kafka-sink-azure-kusto did not sanitize user-controlled values inside the kusto.tables.topics.mapping configuration. The db, table, mapping, and format fields of each mapping entry were interpolated…
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directly into KQL management/query commands via String.formatted(...) (e.g., FETCH_TABLE_COMMAND.formatted(table) → "<table> | count", FETCH_TABLE_MAPPING_COMMAND.formatted(table, format, mapping) → ".show table <table> ingestion <format> mapping '<mapping>'"). An actor able to influence the connector configuration (for example, someone with permissions to submit or edit Kafka Connect connector configs) could embed KQL metacharacters (;, |, ') to execute arbitrary management commands in the context of the connector's service principal — enabling schema enumeration/modification, ingestion-mapping tampering, or changes to streaming/retention policies on the target Azure Data Explorer database. This is a tampering vulnerability. Exploitation requires privileged access to the connector configuration; no end-user interaction or Kafka record payload is involved. This vulnerability is fixed in 5.2.3.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Input validation directly stops unneutralized special elements from reaching query logic.
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 require parameterized queries and input neutralization to prevent query-logic injection.
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 in development catches injection vulnerabilities before release but does not itself implement the fix.
Secure development life cycle mandates input validation and query parameterization that directly prevent injection flaws.
Application security requirements explicitly call for controls against injection and improper query construction.
Secure architecture principles reduce the likelihood of query-logic flaws but do not prescribe the specific coding practice.
Secure coding standards require proper neutralization of special elements in all data queries.
Outsourced development agreements can require secure coding practices, indirectly mitigating the weakness.