threat-modeling.md

Threat Model Analysis for plotly/dash

Threat: Callback Injection via Input Manipulation

• Threat: Callback Injection via Input Manipulation

  • Description: An attacker crafts malicious input (e.g., specially formatted strings, unexpected data types) into a dcc.Input component (or similar, like dcc.Textarea, dcc.Dropdown, etc.) that is used as an argument to a Dash callback. The attacker aims to alter the intended behavior of the callback function, potentially executing arbitrary code on the server, accessing unauthorized data, or modifying application state. The attacker might try to pass data that triggers unexpected branches in the callback logic.
  • Impact:
    • Remote Code Execution (RCE) on the server (if the callback mishandles input and uses it in an unsafe way, like eval).
    • Unauthorized data access or modification.
    • Application state corruption.
    • Denial of Service (if the injected input triggers an infinite loop or resource exhaustion).
  • Affected Dash Component: dash.callback, dcc.Input, dcc.Textarea, dcc.Dropdown, dcc.Slider, and any other component that provides user input to callbacks.
  • Risk Severity: Critical (if RCE is possible), High (otherwise).
  • Mitigation Strategies:
    • Strict Server-Side Input Validation: Implement rigorous validation of all callback inputs on the server-side (in the Python callback function). Check data types, ranges, allowed values, and lengths. Use whitelists whenever possible.
    • Avoid eval and Similar: Never use eval, exec, or similar functions with user-provided input within callbacks.
    • Type Hinting: Use Python type hints to enforce expected data types for callback arguments.
    • dash.callback_context Validation: Use dash.callback_context.triggered to verify the expected component and property triggered the callback. Do not blindly trust the triggered information.
    • Sanitize Input (if necessary): If you must process user input that might contain special characters, use appropriate sanitization libraries (e.g., bleach for HTML) on the server-side.

Threat: Callback Denial of Service (DoS)

• Threat: Callback Denial of Service (DoS)

  • Description: An attacker repeatedly triggers a computationally expensive or resource-intensive Dash callback, overwhelming the server and making the application unresponsive to legitimate users. This could involve triggering a callback that performs a complex calculation, fetches a large amount of data, or interacts with a slow external API. The attacker might use automated tools to send a high volume of requests.
  • Impact:
    • Application unavailability for legitimate users.
    • Server resource exhaustion (CPU, memory, network).
    • Potential financial costs (if using cloud resources).
  • Affected Dash Component: dash.callback, and any component that can trigger a callback (e.g., dcc.Input, dcc.Button, dcc.Interval).
  • Risk Severity: High.
  • Mitigation Strategies:
    • Rate Limiting: Implement rate limiting on callbacks, especially those known to be resource-intensive. This can be done within the Dash application (using a library like Flask-Limiter) or at the web server level (e.g., Nginx).
    • Timeouts: Set timeouts for callback execution to prevent long-running processes from blocking the server indefinitely.
    • Asynchronous Callbacks: Offload long-running or resource-intensive tasks to a background worker queue (e.g., Celery) or use Dash's background callback functionality (if appropriate).
    • Resource Monitoring: Monitor server resource usage to detect and respond to DoS attempts.
    • Input Validation (to limit scope): Validate input to computationally expensive callbacks to limit the scope of the operation (e.g., limit the size of a data request).

Threat: dcc.Store Data Exposure

• Threat: dcc.Store Data Exposure

  • Description: Sensitive data stored in dcc.Store components, particularly with storage_type='memory', might be inadvertently exposed to other users or attackers. This could happen due to misconfiguration, session management vulnerabilities, or if an attacker gains access to the server's memory.
  • Impact:
    • Leakage of sensitive user data (e.g., personal information, session tokens).
    • Potential for session hijacking or impersonation.
    • Loss of confidentiality.
  • Affected Dash Component: dcc.Store.
  • Risk Severity: High (if storing sensitive data).
  • Mitigation Strategies:
    • Use storage_type='session': For user-specific data, use storage_type='session' to isolate data between user sessions.
    • Avoid Storing Sensitive Data: Minimize the storage of highly sensitive data (e.g., passwords, API keys) in dcc.Store. If necessary, encrypt the data before storing it.
    • Secure Session Management: Ensure the underlying Flask session management is configured securely (HTTPS, secure cookies, appropriate timeouts).
    • Server Security: Implement strong server security measures to prevent unauthorized access to the server's memory.

Threat: Unprotected Developer Tools in Production

• Threat: Unprotected Developer Tools in Production

  • Description: If Dash developer tools (e.g., dev_tools_ui=True, dev_tools_props_check=True) are enabled in a production environment, sensitive information and debugging endpoints (like /_dash-update-component) are exposed. Attackers could use these endpoints to gain information about the application's structure, internal state, or potentially exploit vulnerabilities.
  • Impact:
    • Information disclosure (application structure, internal state, component props).
    • Potential for exploitation of vulnerabilities exposed through debugging endpoints.
    • Increased attack surface.
  • Affected Dash Component: app.run_server, app.enable_dev_tools, and related developer tool settings.
  • Risk Severity: High.
  • Mitigation Strategies:
    • Disable Developer Tools in Production: Ensure all dev_tools_* options are set to False in a production environment.
    • Environment Variables: Use environment variables to control developer tool settings, ensuring they are disabled in production deployments.
    • Network Restrictions (if necessary): If developer tools must be enabled in a restricted environment (e.g., a staging server), use network-level restrictions (firewall rules) to limit access to the debugging endpoints.

Threat: Vulnerabilities in Dash or its Dependencies

• Threat: Vulnerabilities in Dash or its Dependencies

  • Description: Dash itself, or its underlying dependencies (Flask, React, Werkzeug, etc.), may contain security vulnerabilities. Attackers could exploit these vulnerabilities to compromise the application.
  • Impact:
    • Varies widely depending on the specific vulnerability (could range from information disclosure to RCE).
    • Application compromise.
    • Data breach.
  • Affected Dash Component: Potentially any component, depending on the vulnerability.
  • Risk Severity: Varies (Low to Critical, depending on the vulnerability), but can be High or Critical.
  • Mitigation Strategies:
    • Keep Software Updated: Regularly update Dash and all of its dependencies to the latest versions. Use a dependency management tool (e.g., pip, poetry) to track and update dependencies.
    • Vulnerability Scanning: Use vulnerability scanning tools (e.g., pip-audit, safety, Snyk, Dependabot) to identify known vulnerabilities in your application's dependencies.
    • Monitor Security Advisories: Stay informed about security advisories and patches released for Dash and its dependencies.