Sensitive data written to disk unencrypted in Spark

Exploitation

No confirmed exploitation recorded. Patch Next marks exploitation only from CISA KEV.

CVSS 4.0

8.7 · CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:N/VA:N/SC:N/SI:N/SA:N · score source

EPSS

• CVE-2019-10099: 1.30% estimated exploitation activity in the next 30 days; percentile 70.51 (FIRST EPSS, 2026-11-03). This is a model estimate, not proof of exploitation or customer exposure.

As published by the source; long descriptions are shortened.

Prior to Spark 2.3.3, in certain situations Spark would write user data to local disk unencrypted, even if spark.io.encryption.enabled=true. This includes cached blocks that are fetched to disk (controlled by spark.maxRemoteBlockSizeFetchToMem); in SparkR, using parallelize; in Pyspark, using broadcast and parallelize; and use of python udfs.

Published by the source

8 Aug 2019, 15:18 UTC

Last modified by the source

24 Oct 2024, 22:35 UTC

Source last checked

26 Oct 2026, 13:22 UTC

1. Revision 3 · amended · 26 Oct 2026

   Amended from ghsa record GHSA-fp5j-3fpf-mhj5

2. Revision 2 · amended · 24 Oct 2026

   Reviewed OSV alias/range/CVSS4 correction; package source scopes and original publication preserved. No customer finding resolved.

3. Revision 1 · created · 24 Oct 2026

   Published from osv record GHSA-fp5j-3fpf-mhj5

Preview the exact text before you copy it. Commands are read-only suggestions; review them before running anything.

# Patch Next advisory brief: CVE-2019-10099

Advisory: Sensitive data written to disk unencrypted in Spark
IDs: CVE-2019-10099, GHSA-fp5j-3fpf-mhj5
Record: https://patchnext.com/advisories/cve-2019-10099 (revision 3, source checked 26 Oct 2026, 13:22 UTC)
Inventory: none attached. This brief comes from a public page and does not say whether any deployment is affected.
Severity: High (CVSS 8.7)
Exploitation: no confirmed exploitation recorded. Patch Next confirms exploitation only from CISA KEV.
Vendor response: Vendor fix available; fixed versions by package: org.apache.spark:spark-core_2.11 2.3.3; pyspark 2.3.3. A vendor fix does not mean a deployment is fixed.

## Affected components
- Maven org.apache.spark:spark-core_2.11: affected < 2.3.3; fixed in 2.3.3
- PyPI pyspark: affected < 2.3.3; fixed in 2.3.3

## Suggested inspection (read-only)
- Maven org.apache.spark:spark-core_2.11: check the installed version with your package manager.
- `python -m pip show pyspark`: prints the installed version. Run with the environment that serves production active.

## Supported action
Upgrade each affected component to a fixed version on its release line. For a transitive dependency, upgrade the parent package or add an override or constraint, then regenerate the lockfile. Redeploy every environment that ran an affected version.

## Required regression checks
- Run the test suite and a production build.
- Exercise the features that depend on org.apache.spark:spark-core_2.11, pyspark.
- Repeat the inspection and confirm that only fixed versions remain installed.

## Rollback considerations
- Keep the previous lockfile and deployment artifact. Rolling back reinstates the vulnerable version, so apply any documented mitigation while you fix forward.

## Unresolved questions
- Which deployments run an affected version?
- Exploitation status can change. Check again before lowering the priority.
- Mark the work resolved only after the fixed version is verified in production.

## Sources
- Verified: https://osv.dev/vulnerability/GHSA-fp5j-3fpf-mhj5 (OSV record GHSA-fp5j-3fpf-mhj5 lists affected ranges: Maven org.apache.spark:spark-core_2.11 >=0 =0 <2.3.3 (fixed))
- Verified: https://github.com/advisories/GHSA-fp5j-3fpf-mhj5 (GitHub-reviewed advisory GHSA-fp5j-3fpf-mhj5 lists affected ranges: Maven org.apache.spark:spark-core_2.11 >=0 =0 <2.3.3)

Commands are suggestions: review each one before running it.