A critical remote code execution (RCE) vulnerability in Apache Parquet’s Java library (CVE-2025-30065), rated with a maximum CVSS score of 10.0, has sent shockwaves through the big data and cloud computing industries.
The flaw, rooted in insecure deserialization within the parquet-avro module, enables attackers to execute arbitrary code by exploiting maliciously crafted Parquet files.
With major platforms like AWS, Google Cloud, and Apache Spark relying on Parquet for data processing, the vulnerability threatens to compromise sensitive analytics workflows and enterprise data pipelines globally.
Technical Breakdown of CVE-2025-30065
Root Cause: The vulnerability stems from improper validation during Avro schema parsing in Apache Parquet’s Java library. Specifically, the parquet-avro The module fails to restrict class instantiation when deserializing untrusted Avro data embedded in Parquet files.
Attackers can embed malicious schemas referencing Java classes with a single String parameter constructor, triggering unintended side effects like network requests or code execution.
Exploitation Mechanics:
- Attack Vector: Delivering a malicious Parquet file to systems processing external data.
- Impact: Arbitrary class instantiation allows attackers to invoke methods with harmful side effects (e.g., HTTP callouts, file system access).
- Key Constraint: Exploitation requires a vulnerable class in the server’s classpath, limiting direct RCE but enabling reconnaissance or data exfiltration.
F5 Labs Releases Canary Exploit Tool
To address patch verification challenges, F5 Labs published a proof-of-concept (PoC) tool that generates a benign Parquet file, triggering an HTTP GET request via the javax.swing.JEditorKit class.
This “canary” approach allows organizations to:
- Detect Exposure: Unpatched systems will emit an HTTP request when processing the file.
- Validate Mitigations: Confirm successful upgrades to Apache Parquet 1.15.1 or later.
- Audit Complex Environments: Identify lingering vulnerabilities in transitive dependencies or legacy systems.
Risk Factor Analysis
| Risk Factor | Description | Severity |
|---|---|---|
| CVSS Score | Maximum 10.0 due to low attack complexity and high impact potential. | Critical |
| Attack Surface | Affects all systems processing Parquet files from untrusted sources. | High |
| Patch Complexity | Dependency trees in big data frameworks may delay upgrades. | Moderate |
| Exploit Availability | Public PoCs increase likelihood of opportunistic attacks. | High |
| Mitigation Effectiveness | Configuring org.apache.parquet.avro.SERIALIZABLE_PACKAGES reduces risk. | High (if applied) |
Recommendations for Organizations
- Immediate Patching: Upgrade to Apache Parquet 1.15.1 or later.
- Deserialization Hardening: Restrict allowable packages using
SERIALIZABLE_PACKAGESand avoid wildcard (*) entries. - File Validation: Scrutinize Parquet files from external sources and sandbox processing.
- Continuous Monitoring: Deploy network intrusion detection for anomalous HTTP requests or file processing activity.
F5 Labs emphasizes that while exploitation is technically challenging, the ubiquity of Parquet in data pipelines demands proactive mitigation.
With major cloud providers and enterprises like Netflix and Airbnb impacted, CVE-2025-30065 underscores the critical need for robust dependency management in modern data ecosystems.
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