CVE-2019-10099: Sensitive data written to disk unencrypted in Spark
(updated )
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.
References
- github.com/advisories/GHSA-fp5j-3fpf-mhj5
- github.com/pypa/advisory-database/tree/main/vulns/pyspark/PYSEC-2019-114.yaml
- lists.apache.org/thread.html/c2a39c207421797f82823a8aff488dcd332d9544038307bf69a2ba9e@%3Cuser.spark.apache.org%3E
- lists.apache.org/thread.html/ra216b7b0dd82a2c12c2df9d6095e689eb3f3d28164e6b6587da69fae@%3Ccommits.spark.apache.org%3E
- lists.apache.org/thread.html/rabe1d47e2bf8b8f6d9f3068c8d2679731d57fa73b3a7ed1fa82406d2@%3Cissues.spark.apache.org%3E
- nvd.nist.gov/vuln/detail/CVE-2019-10099
Code Behaviors & Features
Detect and mitigate CVE-2019-10099 with GitLab Dependency Scanning
Secure your software supply chain by verifying that all open source dependencies used in your projects contain no disclosed vulnerabilities. Dependency Scanning using SBOM also detects vulnerabilities from proprietary advisories that this site does not include. Learn more about Dependency Scanning using SBOM →