In Apache Spark 1.0.0 to 2.1.2, 2.2.0 to 2.2.1, and 2.3.0, when using PySpark or SparkR, it's possible for a different local user to connect to the Spark application and impersonate the user running the Spark application.
This vulnerability carries a MEDIUM severity rating with a CVSS v3.1 score of 4.7, requiring local system access to exploit but requires specific conditions to be met without requiring user interaction requiring only low-level privileges . The vulnerability impacts integrity (unauthorized modifications), for affected systems. Impacting 1 product from apache organizations running these solutions should prioritize assessment and patching.
First disclosed in 2018, this vulnerability was reported during a period defined by widespread IoT adoption challenges, mobile security concerns, and the emergence of advanced persistent threat (APT) techniques. Contemporary mitigation strategies focused on secure development practices and third-party component vetting.
2018-07-12T13:29:00.227
2024-11-21T03:59:38.733
Modified
CVSSv3.0: 4.7 (MEDIUM)
AV:L/AC:M/Au:N/C:N/I:P/A:N
3.4
2.9
| Type | Vendor | Product | Version/Range | Vulnerable? |
|---|---|---|---|---|
| Application | apache | spark | ≤ 2.1.2 | Yes |
| Application | apache | spark | ≤ 2.2.1 | Yes |
| Application | apache | spark | 2.3.0 | Yes |
SecUtils normalizes and enriches National Vulnerability Database (NVD) records by standardizing vendor and product identifiers, aggregating vulnerability metadata from both NVD and MITRE sources, and providing structured context for security teams. For apache's affected products, we extract Common Platform Enumeration (CPE) data, Common Weakness Enumeration (CWE) classifications, CVSS severity metrics, and reference data to enable rapid vulnerability prioritization and asset correlation. This record contains no exploit code, proof-of-concept instructions, or attack methodologies—only defensive intelligence necessary for patch management, risk assessment, and security operations.