threat-modeling.md

Threat Model Analysis for fabiomsr/drawable-optimizer

Threat: Malicious Executable Replacement

• Threat: Malicious Executable Replacement

  • Description: An attacker replaces the legitimate drawable-optimizer executable with a malicious version. The attacker might achieve this through compromised update mechanisms, supply chain attacks, or by gaining write access to the installation directory where drawable-optimizer resides. The malicious executable could then perform any action the attacker desires, such as stealing data, installing malware, or disrupting the system. This directly impacts drawable-optimizer because the attack vector is the replacement of the tool itself.
  • Impact: Complete system compromise, data theft, malware installation, denial of service.
  • Affected Component: The entire drawable-optimizer executable.
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Verify executable checksum (SHA-256) before execution. The application must securely store and compare this checksum.
    • Use a secure package manager with signed packages and pinned versions (e.g., pip with requirements.txt and verification).
    • Run drawable-optimizer in a sandboxed environment (container, restricted user account) to limit the impact of a compromised executable.
    • Regularly audit the installation directory of drawable-optimizer for unauthorized modifications.

Threat: Command-Line Argument Injection

• Threat: Command-Line Argument Injection

  • Description: An attacker manipulates the command-line arguments passed to drawable-optimizer. If the application dynamically constructs these arguments based on user input without extremely careful sanitization, an attacker could inject malicious options. This could lead to unexpected behavior, such as writing files to arbitrary locations, deleting files, or potentially even executing arbitrary commands if drawable-optimizer itself has exploitable vulnerabilities related to how it parses and handles arguments. This is a direct threat because it targets the way the application interacts with the drawable-optimizer CLI.
  • Impact: Denial of service, file system manipulation, potential code execution (contingent on vulnerabilities within drawable-optimizer's argument handling).
  • Affected Component: The command-line argument parsing logic within drawable-optimizer.
  • Risk Severity: High
  • Mitigation Strategies:
    • Hardcode command-line arguments whenever possible. Avoid dynamic construction from user input.
    • If dynamic arguments are absolutely necessary, use a strict whitelist of allowed values and characters. Do not simply escape special characters; whitelisting is crucial.
    • Thoroughly sanitize any user-provided input that must be used in arguments, even after whitelisting. This is a defense-in-depth measure.

Threat: Privilege Escalation via Code Execution (within drawable-optimizer)

• Threat: Privilege Escalation via Code Execution (within drawable-optimizer)

  • Description: A vulnerability within drawable-optimizer itself or one of its direct dependencies (e.g., a buffer overflow in an image parsing library that drawable-optimizer uses internally) allows an attacker to execute arbitrary code. If drawable-optimizer is running with elevated privileges (which it should not be), the attacker could gain those privileges. This is distinct from the "Input Image Tampering" threat because this focuses on vulnerabilities within the optimizer or its direct, internal dependencies, not just the general image processing libraries.
  • Impact: Complete system compromise.
  • Affected Component: Any vulnerable code within drawable-optimizer or its direct, internal dependencies that allows for arbitrary code execution. This is harder to pinpoint without specific vulnerability analysis of the codebase.
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Run drawable-optimizer with the absolute least privileges necessary. Never run it as root or with administrative privileges.
    • Use a sandboxed environment (e.g., a container with a non-root user, a restricted user account) to isolate drawable-optimizer from the rest of the system.
    • Keep drawable-optimizer and its direct dependencies (check its setup.py or equivalent) up-to-date to patch any known security vulnerabilities. This is crucial.
    • Regular security audits and penetration testing, specifically targeting drawable-optimizer and its interaction with the application, are highly recommended.

Threat: Denial of Service (Resource Exhaustion caused by drawable-optimizer)

• Threat: Denial of Service (Resource Exhaustion caused by drawable-optimizer)

  • Description: An attacker provides a crafted input image designed to cause drawable-optimizer itself (or its internal image processing logic) to consume excessive resources (CPU, memory, disk space). This exploits potential inefficiencies or vulnerabilities within the optimizer's code related to how it handles specific image types, sizes, or complexities. This is different from a general DoS on the web application; it's specifically targeting the optimizer's processing.
  • Impact: Denial of service, application unavailability due to the optimizer consuming all available resources.
  • Affected Component: The image processing and optimization algorithms within drawable-optimizer and its direct dependencies. This includes how it handles image loading, resizing, format conversions, and optimization passes.
  • Risk Severity: High
  • Mitigation Strategies:
    • Set resource limits (CPU time, memory usage) on the drawable-optimizer process itself using operating system mechanisms (e.g., ulimit on Linux, resource limits in container orchestration systems). This is the most direct mitigation.
    • Implement timeouts for the drawable-optimizer process to prevent it from running indefinitely on a malicious input.
    • Monitor the resource usage of the drawable-optimizer process and automatically terminate processes that exceed predefined thresholds.