How to remediate – Apache Spark Detection

1. Introduction

The remote web server hosts Apache Spark, an open source data analytics platform. This means a potential attacker could gain information about the system and its configuration. Systems running Apache Spark are usually affected. A successful exploit could lead to information disclosure.

2. Technical Explanation

The remote web server hosts Apache Spark, which is an open source data analytics platform. There isn’t a specific exploit path available in the context provided. However, publicly accessible Spark instances may expose sensitive configuration details or allow unauthorized access to data processing resources.

• Root cause: The Spark master web UI is exposed without authentication.
• Exploit mechanism: An attacker could directly access the Spark master web UI via a standard web browser and gather information about running jobs, configurations, and potentially sensitive data.
• Scope: Apache Spark installations accessible from the internet or untrusted networks are affected.

3. Detection and Assessment

To confirm whether a system is vulnerable, check if the Spark master web UI is publicly accessible. A thorough method involves reviewing network configurations to identify exposed ports and services.

• Quick checks: Use curl or a web browser to access the Spark master web UI (typically on port 8080).
• Scanning: Nessus plugin ID 16974 can be used as an example for detecting exposed Apache Spark instances.
• Logs and evidence: Examine web server logs for requests accessing the Spark master web UI.

curl http://{spark_master_ip}:8080

4. Solution / Remeditation Steps

Provide precise, ordered steps to fix the issue. Make steps small, testable, and safe to roll back. Only include steps that apply to this vulnerability.

4.1 Preparation

• Call out dependencies or pre-requisites: Ensure you have administrative access to the Spark master node. A roll back plan is to restore from the previous snapshot if necessary.
• Mention change window needs and who should approve, if relevant: Coordinate with system owners during a maintenance window.

4.2 Implementation

1. Step 1: Configure authentication for the Spark master web UI by setting a password or using an existing authentication mechanism (e.g., Kerberos).
2. Step 2: Restrict access to the Spark master web UI using firewall rules, allowing only trusted networks and users.

4.3 Config or Code Example

Before

# spark-defaults.conf (no authentication configured)

After

# spark-defaults.conf (authentication configured - example using a password)
spark.ui.auth.enabled=true
spark.ui.auth.password={your_secure_password}

4.4 Security Practices Relevant to This Vulnerability

List only practices that directly address this vulnerability type. Use neutral wording and examples instead of fixed advice. For example: least privilege, input validation, safe defaults, secure headers, patch cadence. If a practice does not apply, do not include it.

• Practice 1: Least privilege to reduce impact if exploited. Limit access to Spark resources only to authorized users and services.
• Practice 2: Network segmentation to block unsafe data. Restrict network access to the Spark master node using firewalls and security groups.

4.5 Automation (Optional)

# Example Ansible task to configure spark-defaults.conf
- name: Configure Spark UI authentication
  copy:
    dest: /path/to/spark-defaults.conf
    content: |
      spark.ui.auth.enabled=true
      spark.ui.auth.password={{ your_secure_password }}
  notify: Restart Spark Master

5. Verification / Validation

Explain how to confirm the fix worked. Provide commands, expected outputs, and a short negative test if possible. Include a simple service smoke test.

• Re-test: Attempt to access the Spark master web UI without providing valid credentials. The request should be blocked or redirected to an authentication page.
• Monitoring: Monitor web server logs for failed authentication attempts, which could indicate unauthorized access attempts.

curl -u {username}:{password} http://{spark_master_ip}:8080 (should return the UI content)

6. Preventive Measures and Monitoring

Suggest only measures that are relevant to the vulnerability type. Use “for example” to keep advice conditional, not prescriptive.

• Baselines: Update a security baseline or policy if it prevents this issue (for example, CIS control 1.2).
• Pipelines: Add checks in CI or deployment to stop the same fault (for example, static code analysis for exposed credentials).

7. Risks, Side Effects, and Roll Back

• Roll back: Restore the original spark-defaults.conf file from the snapshot taken during preparation. Restart the Spark master service.

8. References and Resources

• Vendor advisory or bulletin: https://spark.apache.org/