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PatchSiren cyber security CVE debrief

CVE-2022-33891 Apache CVE debrief

CVE-2022-33891 is a command injection vulnerability in Apache Spark that CISA added to its Known Exploited Vulnerabilities catalog on 2023-03-07. Because it is listed in KEV, defenders should treat it as actively exploited risk and prioritize remediation using vendor guidance.

Vendor
Apache
Product
Spark
CVSS
Unknown
CISA KEV
Listed
Original CVE published
2023-03-07
Original CVE updated
2023-03-07
Advisory published
2023-03-07
Advisory updated
2023-03-07

Who should care

Organizations running Apache Spark, especially teams responsible for data platforms, analytics clusters, and any internet-facing or broadly reachable Spark deployments. Security and operations teams should also prioritize it because CISA has flagged it as known exploited.

Technical summary

The available source corpus identifies the issue as a command injection vulnerability affecting Apache Spark. CISA’s KEV entry indicates it is a known exploited vulnerability and directs users to apply updates per vendor instructions. No affected-version range, exploitation path, or additional technical preconditions are provided in the supplied sources.

Defensive priority

High. KEV inclusion indicates known exploitation and a CISA remediation deadline of 2023-03-28 in the source metadata. Remediation should be prioritized ahead of routine maintenance, especially for exposed or production Spark environments.

Recommended defensive actions

  • Identify all Apache Spark deployments, including managed services and embedded Spark components.
  • Review the Apache and CVE/NVD records for vendor remediation guidance and applicable fixed releases.
  • Apply the vendor-recommended updates or mitigations as soon as possible, with special attention to systems reachable from untrusted networks.
  • Restrict access to Spark interfaces to trusted administrative networks and approved service accounts where possible.
  • Validate remediation by confirming affected components are updated across all environments, including development, staging, and production.
  • Monitor logs and platform telemetry for suspicious command execution or unexpected administrative activity around Spark services.

Evidence notes

This debrief is based only on the supplied source corpus and official links. The key supporting facts are: CISA KEV lists CVE-2022-33891 as 'Apache Spark Command Injection Vulnerability'; the KEV metadata marks it as known exploited and provides the remediation note 'Apply updates per vendor instructions'; the CVE/NVD links are included as official reference points. No further version, exploit, or impact details were supplied.

Sources and references

Verified primary and authoritative sources

  • CVE-2022-33891 CVE Program record

    Publisher, destination, and source semantics verified

    URL: https://www.cve.org/CVERecord?id=CVE-2022-33891

    CVE Program - Official CVE Program record with source-provided CVE metadata.

  • CVE-2022-33891 NVD vulnerability detail

    Publisher, destination, and source semantics verified

    URL: https://nvd.nist.gov/vuln/detail/CVE-2022-33891

    NIST National Vulnerability Database - Official NIST NVD detail page and source-specific vulnerability assessment.

  • CISA Known Exploited Vulnerabilities catalog

    Publisher, destination, and source semantics verified

    URL: https://www.cisa.gov/known-exploited-vulnerabilities-catalog

    Cybersecurity and Infrastructure Security Agency - Official CISA catalog of vulnerabilities known to be exploited in the wild.

Supplemental references

  • Source item URL

    Unverified legacy reference

    URL: https://www.cisa.gov/sites/default/files/feeds/known_exploited_vulnerabilities.json

    cisa_kev

Methodology and review provenance

AI-assisted synthesis based on stored public vulnerability evidence. System validation, approval state, and publication status do not by themselves establish human review of this revision. PatchSiren helps prioritize defensive review and does not prove exposure or remediation on any system.