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SparkApplication · Apache

CVE-2019-10099

HIGH · 7.5 CVSS v3.1 Published 2019-08-07
Fix available
A fix is available. Upgrade to 2.3.2 or later.
See remediation →
84/100
Remediation priority · High
Remotely reachable No privileges Zero-click

Official description Straight from the sourceThe vendor's or NVD's own wording, published unedited. Authoritative, but often terse — it says what broke, rarely what to do.

NVD · unedited
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.

Technical summary Written by usOur analysis, written from the advisory, the CVSS vector and the affected-version data. It adds context the advisory leaves out, and never invents facts that are not in the source.

dbcve analysis · high confidence

In Apache Spark versions prior to 2.3.3, the encryption feature (spark.io.encryption.enabled=true) could be bypassed, causing user data to be written unencrypted to local disk. This affects cached blocks transferred to disk via spark.maxRemoteBlockSizeFetchToMem, SparkR parallelize operations, PySpark broadcast/parallelize operations, and Python UDFs.

MitigationUpgrade to Apache Spark 2.3.3 or later where the encryption bypass is fixed, or review and restrict the use of affected features until upgrade is possible.

Verify against the referenced sources before acting — the references below are authoritative for this CVE, this summary is not.

Affected products & versions What the vendor confirmedThe version ranges the vendor confirmed as vulnerable. If your version sits inside a range here, treat yourself as exposed until you have upgraded.

NVD · CPE data
SparkApplication
Affected:>= 1.0.2, <= 1.6.3>= 2.0.0, <= 2.0.2>= 2.1.0, <= 2.1.3>= 2.2.0, <= 2.2.2>= 2.3.0, < 2.3.2

CVSS breakdown How the score is builtThe industry scoring standard. It rates how the flaw is reached, what it takes to exploit, and what an attacker gains — the score is derived from those, not the other way round.

From the vector
Attack vector
Network
Complexity
Low
Privileges
None
User interaction
None
Scope
Unchanged
Confidentiality
High
Integrity
None
Availability
None

CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N

Am I affected? How to checkSteps we derive from the advisory and the affected-version data, so you can decide whether this CVE reaches your setup. They are a guide, not a scan — your own configuration is the authority.

dbcve checks

Work through these to decide whether this CVE applies to you.

  1. Check Apache Spark version
    Run `spark-submit --version` or check the spark-core jar filename, or look for the version in the Spark UI under 'Environment' tab
    Affected if Version falls within ranges: 1.0.2 to 1.6.3, 2.0.0 to 2.0.2, 2.1.0 to 2.1.3, 2.2.0 to 2.2.2, or 2.3.0 to 2.3.1
  2. Verify spark.io.encryption.enabled setting
    Check your Spark configuration files (spark-defaults.conf, spark-env.sh) or run in Spark shell: `spark.conf.get("spark.io.encryption.enabled")`
    Affected if Value is set to true (the encryption bypass only applies when encryption is explicitly enabled)
  3. Check if spark.maxRemoteBlockSizeFetchToMem is in use
    Inspect your Spark config for spark.maxRemoteBlockSizeFetchToMem setting or review application configurations
    Affected if This config is set and causes remote blocks larger than the threshold to be written to disk unencrypted when encryption is enabled
  4. Identify SparkR parallelize operations
    Review SparkR application code for calls to `parallelize()` function which may write data unencrypted to local disk
    Affected if SparkR parallelize operations are being executed in the environment
  5. Identify PySpark broadcast or parallelize operations
    Review PySpark application code for `broadcast()` or `parallelize()` calls which may write data unencrypted to local disk
    Affected if PySpark broadcast or parallelize operations are being executed in the environment
  6. Identify Python UDF usage
    Review application code for registration and execution of Python UDFs via `spark.udf.register()` or similar
    Affected if Python UDFs are being used, as these can cause data to be written unencrypted to local disk

You are affected if you run a vulnerable Spark version (1.0.2-1.6.3, 2.0.0-2.0.2, 2.1.0-2.1.3, 2.2.0-2.2.2, or 2.3.0-2.3.1) with spark.io.encryption.enabled=true and use any of the affected features: spark.maxRemoteBlockSizeFetchToMem, SparkR parallelize, PySpark broadcast/parallelize, or Python UDFs.

Generated from the published advisory. Verify against your own configuration.

Check your environment

Paste your version and any relevant configuration and it will be compared against the affected criteria above. Do not include secrets or credentials.

AI-assisted, checked against the advisory. Informational, not a guarantee.

Remediation Closing itWhat it takes to close this. Where a vendor fix exists we point at it; where none exists we say so plainly, and can build one. Effort estimates are scoped from the advisory, not from your codebase.

From vendor data
Upgrade available Upgrade to 2.3.2 or later
Fixed in 2.3.2
Interim mitigation

Upgrade to Apache Spark 2.3.3 or later where the encryption bypass is fixed, or review and restrict the use of affected features until upgrade is possible.

Fix this in Spark Scoped from the published advisory
  • Consultation2.0 h
  • Implementation4.0 h
  • Testing3.0 h
  • Review / QA2.0 h
11.0 hours of engineering $1,930
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References Go to the primary sourcePrimary sources — vendor advisories, patches and trackers. Where our summary and a reference disagree, the reference wins.

Primary sources

Practitioner notes

Contributed

Peer-ranked notes from engineers who’ve handled CVE-2019-10099 in production — separate from our analysis above.

No notes yet

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What this is

A place for practitioners to share what actually worked: a mitigation you’ve tested, a configuration change, a version- or environment-specific caveat, or a link to a verified patch. The most useful notes rise to the top as peers upvote them, so the signal stays high.

What belongs here
  • Verified mitigations, workarounds, and config changes
  • Version or environment caveats, and links to real fixes
  • No weaponised exploit code, or anything meant to cause harm
  • No spam, self-promotion, credentials, or personal data