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attack-surface.md

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Attack Surface Analysis for elastic/logstash

Attack Surface: Unvalidated Input Data in Input Plugins

  • Description: Input plugins ingest data from various sources. Lack of input validation allows malicious or malformed data to be processed, potentially leading to injection attacks or DoS within Logstash or downstream systems.
  • Logstash Contribution: Logstash relies on input plugins to receive data. If plugins are not configured or designed to validate input, Logstash becomes vulnerable to processing malicious data.
  • Example: A Logstash pipeline uses the tcp input plugin. An attacker sends crafted log messages containing escape sequences or control characters that exploit vulnerabilities in downstream processing or logging systems.
  • Impact: Command injection, log injection, denial of service, data corruption, exploitation of downstream systems.
  • Risk Severity: High
  • Mitigation Strategies:
    • Implement Input Validation: Configure input plugins to validate and sanitize all incoming data based on expected formats and data types. Utilize plugin-specific validation options if available.
    • Network Segmentation: Isolate Logstash instances and input sources within secure network zones to limit exposure.
    • Regular Plugin Updates: Ensure all input plugins are updated to the latest versions to patch known vulnerabilities and benefit from security improvements.

Attack Surface: Code Execution in Filter Plugins (e.g., ruby filter)

  • Description: Filter plugins that allow custom code execution, such as the ruby filter, introduce a critical attack surface if configurations are compromised or poorly managed. This can lead to arbitrary code execution within the Logstash process.
  • Logstash Contribution: Logstash's architecture allows for flexible data manipulation through filter plugins, including the ruby filter which enables direct code execution within the pipeline.
  • Example: An attacker gains unauthorized write access to Logstash configuration files and injects malicious Ruby code into a ruby filter within a pipeline. This code could be designed to establish a reverse shell, exfiltrate data, or disrupt Logstash operations.
  • Impact: Arbitrary code execution on the Logstash server, full system compromise, data exfiltration, denial of service, lateral movement within the network.
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Minimize Use of Code Execution Filters: Avoid using filter plugins that allow arbitrary code execution (like ruby) unless absolutely necessary and only when no safer alternatives exist.
    • Strict Configuration Security: Implement robust access control and change management for Logstash configuration files. Use version control, code review, and restrict write access to authorized personnel only.
    • Input Sanitization Before Code Execution: If code execution filters are unavoidable, rigorously sanitize and validate input data before it is processed by these filters to prevent injection of malicious code through input.
    • Principle of Least Privilege: Run the Logstash process with the minimum necessary privileges to limit the impact of a successful code execution exploit.

Attack Surface: Injection Vulnerabilities in Output Plugins

  • Description: Output plugins write processed data to external systems. If output plugins do not properly sanitize data before writing, they can introduce injection vulnerabilities in these destination systems, such as SQL injection in databases or command injection in file systems.
  • Logstash Contribution: Logstash pipelines are designed to forward data to various output destinations. If output plugins lack proper output sanitization, Logstash can become a vector for propagating injection attacks to downstream systems.
  • Example: A Logstash pipeline outputs data to a SQL database using the jdbc output plugin. Log data contains unsanitized user-controlled strings. The jdbc output plugin constructs SQL queries without proper parameterization, leading to SQL injection vulnerabilities in the target database.
  • Impact: SQL injection, NoSQL injection, command injection in output destinations, data corruption in destination systems, unauthorized access to output destinations and potentially wider network access.
  • Risk Severity: High
  • Mitigation Strategies:
    • Implement Output Sanitization: Configure output plugins to sanitize data before writing to external systems. Utilize plugin options for data escaping, parameterization, and prepared statements where available and appropriate for the output destination.
    • Principle of Least Privilege (Output Destinations): Grant Logstash only the minimum necessary permissions to write to output destinations, limiting potential damage from compromised output operations.
    • Regular Plugin Updates: Keep output plugins updated to benefit from security patches and improvements in output sanitization practices.
    • Security Hardening of Output Destinations: Ensure that systems receiving data from Logstash (e.g., databases, Elasticsearch) are themselves properly secured against injection attacks as a defense-in-depth measure.

Attack Surface: Insecure Storage of Credentials and Sensitive Configuration

  • Description: Logstash configuration files and environment variables can contain sensitive information like database passwords, API keys, and other secrets required for input and output plugins. Insecure storage of these secrets makes them vulnerable to unauthorized access.
  • Logstash Contribution: Logstash requires credentials to interact with various systems. Storing these credentials insecurely within Logstash configurations directly exposes this sensitive information.
  • Example: API keys for a cloud monitoring service used by an output plugin are stored in plain text within the Logstash pipeline configuration file. An attacker gains read access to the Logstash server and configuration files, retrieves the API keys, and gains unauthorized access to the cloud monitoring service, potentially leading to data breaches or service disruption.
  • Impact: Unauthorized access to external systems and services, data breaches, privilege escalation, compromise of connected infrastructure.
  • Risk Severity: High
  • Mitigation Strategies:
    • Secure Credential Management: Never store credentials in plain text in Logstash configuration files.
    • Utilize Secrets Management Systems: Employ dedicated secrets management systems (like HashiCorp Vault, AWS Secrets Manager, Azure Key Vault) to securely store and retrieve sensitive credentials. Configure Logstash to fetch credentials from these systems at runtime.
    • Environment Variables: Use environment variables to pass sensitive information to Logstash, ensuring they are managed securely within the deployment environment.
    • File System Permissions: Restrict access to Logstash configuration files using strict file system permissions, limiting read access to only authorized users and processes.
    • Configuration Encryption at Rest: Consider encrypting Logstash configuration files at rest to provide an additional layer of protection for sensitive data.

Attack Surface: Vulnerabilities in Logstash Core, Plugins, and Dependencies

  • Description: Logstash core software, its plugins, and underlying dependencies (like Java libraries) can contain security vulnerabilities. Unpatched vulnerabilities can be exploited to compromise the Logstash instance and potentially the wider infrastructure.
  • Logstash Contribution: Logstash's functionality relies on its core code, a wide range of plugins, and numerous dependencies. Vulnerabilities in any of these components can directly impact Logstash's security posture.
  • Example: A critical remote code execution vulnerability is discovered in the Logstash core or a widely used plugin. If Logstash instances are not promptly updated to patch this vulnerability, attackers can exploit it to gain arbitrary code execution on the Logstash server, potentially leading to full system compromise.
  • Impact: Arbitrary code execution, full system compromise, denial of service, data breaches, lateral movement within the network.
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Implement Regular Updates and Patching: Establish a robust process for regularly updating Logstash core, plugins, and dependencies to the latest versions, prioritizing security patches.
    • Vulnerability Scanning and Monitoring: Regularly scan Logstash and its dependencies for known vulnerabilities using vulnerability scanning tools. Monitor security advisories from Elastic and the wider open-source community.
    • Automated Patching: Implement automated patching processes where feasible to ensure timely application of security updates.
    • Security Monitoring and Alerting: Set up security monitoring and alerting for Logstash instances to detect and respond to potential exploitation attempts.