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Attack Surface Analysis for fluent/fluentd

Attack Surface: Input Plugin Vulnerabilities

Description: Vulnerabilities within input plugins that process data from various sources. These can include buffer overflows, injection flaws, or deserialization issues. Fluentd Contribution: Fluentd's plugin-based architecture relies on numerous input plugins, increasing the likelihood of encountering vulnerabilities in third-party or less-maintained plugins. Example: A vulnerable in_http plugin with a buffer overflow. An attacker sends a specially crafted HTTP request with an overly long header, causing a buffer overflow in the plugin, potentially leading to remote code execution on the Fluentd server. Impact: Remote Code Execution, Denial of Service, Information Disclosure. Risk Severity: High to Critical Mitigation Strategies:

  • Plugin Selection: Use only well-maintained and reputable input plugins. Prefer plugins from the official Fluentd ecosystem or those with strong community support and security records.
  • Regular Updates: Keep all Fluentd plugins updated to the latest versions to patch known vulnerabilities.
  • Vulnerability Scanning: Regularly scan Fluentd and its plugins for known vulnerabilities using security scanning tools.
  • Input Validation: Implement input validation and sanitization within the application sending data to Fluentd, even if plugins are expected to handle it.

Attack Surface: Code Injection in Filter Plugins

Description: Filter plugins that allow users to define custom logic (e.g., using embedded scripting languages) can be vulnerable to code injection if not properly sandboxed. Fluentd Contribution: Fluentd's flexibility allows for custom filter logic, which, if implemented insecurely, can introduce code injection vulnerabilities. Example: A filter plugin using Ruby's eval() function to process log data based on user-provided configuration. An attacker can manipulate the configuration to inject malicious Ruby code that gets executed by Fluentd. Impact: Remote Code Execution, Data Exfiltration, Privilege Escalation. Risk Severity: High to Critical Mitigation Strategies:

  • Avoid Dynamic Code Execution: Minimize or eliminate the use of dynamic code execution features in filter plugins. Prefer declarative configuration or well-tested, pre-built filter plugins.
  • Input Sanitization for Dynamic Logic: If dynamic logic is unavoidable, rigorously sanitize and validate any user-provided input that influences the execution of this logic.
  • Plugin Review: Carefully review the code of filter plugins that use dynamic logic, especially if they are custom-built or from untrusted sources.

Attack Surface: Exposure of Credentials in Configuration Files

Description: Storing sensitive credentials (passwords, API keys) directly in Fluentd configuration files, making them vulnerable to exposure if the configuration is compromised. Fluentd Contribution: Fluentd configuration often requires credentials for input and output plugins. Storing these directly in configuration files is a common misconfiguration within Fluentd deployments. Example: An out_s3 plugin configuration that includes the AWS access key and secret key directly in the fluent.conf file. If an attacker gains access to this file, they can obtain the AWS credentials. Impact: Unauthorized Access to Output Destinations, Data Breaches, Cloud Account Compromise. Risk Severity: High to Critical Mitigation Strategies:

  • Secrets Management: Use dedicated secrets management solutions (e.g., HashiCorp Vault, AWS Secrets Manager, Kubernetes Secrets) to store and manage credentials securely.
  • Environment Variables: Utilize environment variables to pass credentials to Fluentd instead of hardcoding them in configuration files.
  • Configuration File Protection: Restrict access to Fluentd configuration files to authorized personnel and processes only. Use appropriate file system permissions.