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Threat Model Analysis for chartjs/chart.js

Threat: XSS via Custom Plugins or Callbacks

  • Threat: Cross-Site Scripting (XSS) via Custom Plugins or Callbacks.
  • Description: An attacker exploits a vulnerability in a custom Chart.js plugin or a callback function that handles user-provided data. The attacker injects malicious JavaScript code that is executed in the context of the victim's browser. The vulnerability exists because the plugin or callback directly inserts unescaped user input into the DOM, using Chart.js's provided mechanisms.
  • Impact:
    • Client-Side Code Execution: The attacker's JavaScript code runs in the victim's browser, potentially allowing the attacker to steal cookies, redirect the user, or deface the page.
  • Chart.js Component Affected:
    • Any custom plugin that interacts with the DOM.
    • Any callback function that receives user-provided data and inserts it into the DOM (e.g., tooltip callbacks, label formatters). Specifically:
      • options.plugins.tooltip.callbacks.label
      • options.plugins.tooltip.callbacks.title
      • options.scales[scaleId].ticks.callback
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Plugin Auditing: Thoroughly review the code of any third-party plugins for potential XSS vulnerabilities. Prioritize well-maintained and widely-used plugins.
    • Safe DOM Manipulation: In callback functions, never use innerHTML or similar methods to insert user-provided data directly into the DOM. Use textContent or create DOM elements using safe methods (e.g., document.createElement(), element.setAttribute()).
    • Input Sanitization: Sanitize user-provided data before passing it to callback functions or using it within plugins.
    • Content Security Policy (CSP): Implement a strong CSP to restrict script sources, mitigating XSS impact.

Threat: Dependency Confusion / Supply Chain Attack

  • Threat: Dependency Confusion / Supply Chain Attack.
  • Description: An attacker publishes a malicious package with the same name as Chart.js (or a very similar name) to a public package repository (e.g., npm). If the build process is misconfigured or a developer makes a typo, the malicious package might be downloaded and installed instead of the legitimate Chart.js library.
  • Impact:
    • Client-Side Code Execution: The malicious package could contain arbitrary code that runs in the user's browser, giving the attacker full control.
    • Data Exfiltration: The malicious package could send chart data, user data, or other application data to an attacker-controlled server.
  • Chart.js Component Affected: The entire Chart.js library.
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Use a Package Manager with Integrity Checks: Use npm or yarn with package-lock.json or yarn.lock. These files record the exact hash of each installed package, ensuring that the same version is always installed.
    • Pin Dependencies: Specify exact versions of Chart.js and all its dependencies in your project's configuration files (e.g., package.json). Avoid using version ranges (e.g., ^ or ~) that could allow unexpected updates.
    • Regularly Audit Dependencies: Periodically review your project's dependencies to identify any outdated or potentially vulnerable packages. Use tools like npm audit or yarn audit.
    • Private Package Repositories: For highly sensitive projects, consider using a private package repository (e.g., Verdaccio, Nexus Repository OSS) to host your own vetted versions of Chart.js and other dependencies. This gives you complete control over the packages used in your project.
    • Scope Packages: If available, use scoped packages (e.g., @chartjs/chart.js) to reduce the risk of name collisions.

Threat: Malicious Data Injection into Chart Configuration

  • Threat: Malicious Data Injection into Chart Configuration.
  • Description: An attacker provides crafted input that is directly used to construct the Chart.js configuration object. This could involve manipulating numerical values to cause extreme calculations, injecting invalid data types, or providing excessively long strings. The attacker targets any input vector that feeds directly into the new Chart() constructor or the chart.options object.
  • Impact:
    • Client-Side Denial of Service (DoS): The browser freezes or crashes due to excessive memory or CPU usage when Chart.js attempts to process the malicious configuration.
    • Unexpected Chart Behavior: Although less severe than XSS, incorrect rendering can still be disruptive.
  • Chart.js Component Affected:
    • Chart constructor: The initial configuration object.
    • chart.options: The options property, which controls chart behavior.
    • Any plugin configuration options.
  • Risk Severity: High
  • Mitigation Strategies:
    • Strict Input Validation: Validate all data used in the chart configuration. Check data types, ranges, and lengths. Use a whitelist approach.
    • Data Sanitization: Sanitize string data to remove or escape potentially harmful characters.
    • Type Enforcement: Ensure data conforms to expected types. Use TypeScript if possible.
    • Limit Data/Configuration Size: Impose limits on dataset size and configuration complexity.

Threat: Malicious Data Injection into Chart Data

  • Threat: Malicious Data Injection into Chart Data
  • Description: An attacker provides crafted input that is used directly as the data displayed in the chart. This involves injecting extremely large numbers, non-numeric values into numeric fields, or excessively long strings for labels, targeting the data source that feeds directly into chart.data.datasets or chart.data.labels.
  • Impact:
    • Client-Side Denial of Service (DoS): Browser freezes or crashes due to excessive memory or CPU usage during rendering.
    • Unexpected Chart Behavior: Incorrect or nonsensical chart rendering.
  • Chart.js Component Affected:
    • chart.data.datasets: The datasets array, specifically the data property of each dataset.
    • chart.data.labels: The labels array.
  • Risk Severity: High
  • Mitigation Strategies:
    • Strict Input Validation: Validate all data points. Check data types, ranges, and lengths.
    • Data Sanitization: Sanitize string data used for labels or tooltips.
    • Limit Data Size: Impose limits on the number of data points and the size of individual values.
    • Data Type Enforcement: Ensure data conforms to expected types for each dataset.