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Attack Tree Analysis for sirupsen/logrus

Objective: Disrupt, Degrade, or Exfiltrate Data from an Application Using Logrus

Attack Tree Visualization

[Root: Compromise Application Using Logrus] ├── [1. Information Disclosure] [HR] │ ├── [1.1. Sensitive Data in Logs] [CN] │ │ ├── [1.1.1. Developer Error: Logging Secrets] [CN] [HR] │ │ └── [1.1.2. Developer Error: Logging PII] [CN] [HR] ├── [2. Denial of Service (DoS)] │ ├── [2.1. Disk Exhaustion] │ │ └── [2.1.1. Uncontrolled Log Growth] [CN] [HR] │ └── [2.2. Resource Exhaustion (CPU/Memory)] │ └── [2.2.2. Large Log Entries] [HR] └── [3. Log Manipulation/Spoofing] ├── [3.1. Log Injection] │ └── [3.1.1. Unescaped User Input] [CN] [HR]

Attack Tree Path: 1. Information Disclosure [HR]

  • 1.1. Sensitive Data in Logs [CN]

    • This is a critical node because it's a common source of high-impact vulnerabilities. Developers often inadvertently log sensitive information.

    • 1.1.1. Developer Error: Logging Secrets [CN] [HR]

      • Description: Developers mistakenly include API keys, passwords, database credentials, or other secrets directly in log messages.
      • Exploit: An attacker gains access to log files (through filesystem access, a compromised logging service, or other means) and extracts the secrets.
      • Likelihood: Medium
      • Impact: Very High
      • Effort: Very Low
      • Skill Level: Novice
      • Detection Difficulty: Medium
      • Mitigation:
        • Implement rigorous code reviews to identify and remove any instances of secrets being logged.
        • Use secrets management tools (e.g., HashiCorp Vault, AWS Secrets Manager, Azure Key Vault) to store and manage secrets securely. Never hardcode secrets in the application code or configuration.
        • Employ static analysis tools that can detect potential secrets in code and configuration files.
        • Provide developer training on secure coding practices, emphasizing the dangers of logging sensitive data.
        • Avoid logging any data that could be considered a secret.

    • 1.1.2. Developer Error: Logging PII [CN] [HR]

      • Description: Developers log Personally Identifiable Information (PII) such as names, addresses, email addresses, social security numbers, credit card numbers, etc.
      • Exploit: Similar to secret exposure, an attacker gains access to log files and extracts the PII.
      • Likelihood: High
      • Impact: High
      • Effort: Very Low
      • Skill Level: Novice
      • Detection Difficulty: Medium
      • Mitigation:
        • Implement a data minimization strategy: only log the data that is absolutely necessary for debugging and operational purposes.
        • Use PII detection and masking tools in the logging pipeline. These tools can automatically identify and redact PII before it's stored in logs.
        • Provide developer training on privacy regulations (e.g., GDPR, CCPA) and the importance of protecting PII.
        • Regularly audit log data for PII and remove any unnecessary information.

Attack Tree Path: 2. Denial of Service (DoS)

  • 2.1. Disk Exhaustion

    • 2.1.1. Uncontrolled Log Growth [CN] [HR]
      • Description: The application logs excessively without any mechanism for log rotation or size limits, eventually filling up the available disk space.
      • Exploit: An attacker can trigger excessive logging by sending malformed requests, causing errors, or exploiting other vulnerabilities that result in increased log output.
      • Likelihood: Medium
      • Impact: High
      • Effort: Low
      • Skill Level: Novice
      • Detection Difficulty: Easy
      • Mitigation:
        • Implement log rotation: configure Logrus (or the underlying logging system) to automatically rotate log files based on size or time. Older log files can be archived or deleted.
        • Set size limits for log files: prevent individual log files from growing beyond a certain size.
        • Monitor disk space usage: set up alerts to notify administrators when disk space is running low.
        • Implement rate limiting of log events: if an attacker is attempting to flood the logs, rate limiting can prevent excessive log output.

  • 2.2. Resource Exhaustion (CPU/Memory)

    • 2.2.2. Large Log Entries [HR]
      • Description: The application logs very large data structures or strings, consuming significant memory and potentially CPU resources.
      • Exploit: An attacker sends large inputs to the application that are then logged, leading to memory exhaustion or performance degradation.
      • Likelihood: Medium
      • Impact: Medium
      • Effort: Low
      • Skill Level: Novice
      • Detection Difficulty: Medium
      • Mitigation:
        • Implement strict input validation: reject any input that is excessively large or contains unexpected characters.
        • Limit the size of log entries: truncate or sanitize large data before logging it.
        • Sanitize input before logging: remove any unnecessary or potentially harmful data from the input before it's included in log messages.

Attack Tree Path: 3. Log Manipulation/Spoofing

  • 3.1. Log Injection

    • 3.1.1. Unescaped User Input [CN] [HR]
      • Description: The application logs user-provided input without properly escaping or sanitizing it. This allows an attacker to inject newline characters, control characters, or other special sequences into the log messages.
      • Exploit: An attacker can inject false log entries, modify existing entries, or disrupt log parsing and analysis. This can be used to mislead investigations, cover up malicious activity, or potentially exploit vulnerabilities in log analysis tools.
      • Likelihood: Medium
      • Impact: Medium
      • Effort: Low
      • Skill Level: Novice
      • Detection Difficulty: Medium
      • Mitigation:
        • Always sanitize user input before logging it. This includes escaping special characters, removing control characters, and validating the input against expected formats.
        • Use structured logging (JSON format): JSON is inherently less susceptible to log injection because it uses a well-defined structure. Log analysis tools can easily parse JSON logs and identify any unexpected or malicious data.
        • Avoid logging raw user input directly. Instead, log specific fields or values that have been validated and sanitized.