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☕ Maven🐍 PyPI
Not in CISA KEV
HIGH severity

Sensitive data written to disk unencrypted in SparkGHSA-fp5j-3fpf-mhj5

HIGH

GHSA-fp5j-3fpf-mhj5 is a high-severity (CVSS 7.5) CWE-312 vulnerability in org.apache.spark:spark-core_2.11. A fix is available for org.apache.spark:spark-core_2.11 — see the affected versions and patch details below.

Also known asCVE-2019-10099PYSEC-2019-114
Published
Updated
Affected
2 pkgs
Patched
2 / 2
Exploits
None indexed
Exploitation data as of Oct 24, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

EPSS Exploitation Probability

via FIRST.org ↗
1.3%probability of exploitation in next 30 days
Lower Risk0.00%
Lower risk than most CVEs70th percentile — riskier than 70% of all scored CVEsHighest risk
0.00%0.60%1.20%1.80%0.5%1.3%Apr 26Aug 26Oct 26

Probability of exploitation in the next 30 days, from FIRST.org EPSS.

How urgent is this, really

GHSA-fp5j-3fpf-mhj5 by exploitation likelihood (EPSS) against impact (CVSS). Outside the shaded patch-first corner.

Where this sits among everything scored

Of 395,249 CVEs with a current EPSS score, this one falls in the < 10% band (highlighted). Counts from FIRST.org, log-scaled.

Real-World Exposure

2 pkgs affected
☕org.apache.spark:spark-core_2.11🐍pyspark

Real-time download stats are indexed for npm and PyPI packages. This vulnerability affects Maven, PyPI packages — download data is not available via public APIs for these ecosystems.

Description

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.

Affected Packages

2 total 2 fixed
EcosystemPackageVulnerable rangeFix
☕Mavenorg.apache.spark:spark-core_2.11all versions2.3.3org.apache.spark:spark-core_2.11:2.3.3
🐍PyPIpysparkall versions2.3.3pip install --upgrade 'pyspark==2.3.3'

Affected Products

1 product · 5 configurations
Application
sparkapache
≥ 2.3.0 && ≤ 2.2.2
range

Detection & mitigation playbook

Open-source dependency

  1. Detect

    Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for org.apache.spark:spark-core_2.11, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.

  2. Fix

    Update org.apache.spark:spark-core_2.11 to 2.3.3 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-fp5j-3fpf-mhj5 is resolved across your whole dependency graph.

  3. Workarounds

    If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.

Frequently Asked Questions

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.
O3 Security · Impact-Aware SCA

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Sensitive data written to disk unencrypted in Spark