Skip to content

Latest commit

 

History

History
42 lines (35 loc) · 4 KB

threat-modeling.md

File metadata and controls

42 lines (35 loc) · 4 KB

Threat Model Analysis for qos-ch/slf4j

Threat: Sensitive Data Exposure in Logs

  • Threat: Sensitive Data Exposure in Logs

    • Description: An attacker gains access to log files. The attacker then scans the logs for sensitive information that was inadvertently included by developers through their use of SLF4J logging calls. This is the core issue: developers are using SLF4J to log sensitive data.
    • Impact:
      • Exposure of PII, leading to identity theft, financial fraud, or reputational damage.
      • Compromise of authentication credentials.
      • Disclosure of internal system details.
      • Violation of privacy regulations.
    • SLF4J Component Affected:
      • The application code that uses SLF4J's API (e.g., Logger.info(), Logger.error(), etc.). The vulnerability exists because developers are passing sensitive data as arguments to these methods. The underlying logging implementation's appenders then write this data.
    • Risk Severity: Critical
    • Mitigation Strategies:
      • Data Masking/Sanitization: Implement robust data masking before data is passed to SLF4J methods. This could involve custom wrapper classes around SLF4J or pre-processing of data. Configuration-based masking in the logging implementation is a secondary defense, as it relies on correct configuration. The primary defense is to never pass sensitive data to SLF4J in the first place.
      • Strict Logging Policies and Developer Training: Enforce strict guidelines and provide training on what cannot be logged.
      • Code Reviews: Mandatory code reviews to specifically check for logging of sensitive data before it reaches SLF4J calls.
      • Automated Scanning: Use static analysis tools to detect potential logging of sensitive data during development, focusing on calls to SLF4J methods.
      • Parameterized Logging: Strictly enforce parameterized logging. This makes it easier to identify and potentially mask sensitive data before it's passed to the logging framework.

Threat: Denial of Service via Log Flooding (Indirect, but related to SLF4J usage)

  • Threat: Denial of Service via Log Flooding (Indirect, but related to SLF4J usage)

    • Description: An attacker triggers a large volume of log messages through the application's use of SLF4J. While the underlying implementation handles the writing, the application's code, using SLF4J, is the source of the flood. This could be due to a bug exploited by the attacker or intentional malicious requests.
    • Impact:
      • Disk space exhaustion.
      • I/O bottlenecks.
      • Potential CPU overhead.
      • Increased costs (cloud logging).
    • SLF4J Component Affected:
      • The application code that uses SLF4J's API. The vulnerability is that the application, through its use of SLF4J, generates an excessive volume of log messages. The underlying implementation is the target of the flood, but the source is the application's SLF4J usage.
    • Risk Severity: High
    • Mitigation Strategies:
      • Appropriate Log Levels: Use appropriate log levels in production. Avoid DEBUG/TRACE.
      • Asynchronous Logging: Use asynchronous appenders (in the underlying implementation). This doesn't prevent the flood generation, but it mitigates the impact on the application's main thread.
      • Rate Limiting (Advanced): If the underlying logging implementation supports it, configure rate limiting. This is a mitigation at the implementation level, but it addresses the flood caused by the application's SLF4J usage.
      • Input Validation: Thoroughly validate all user input to prevent malicious data from triggering excessive logging through SLF4J calls.
      • Monitoring: Monitor disk space, I/O, and log volume.