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Security Policy for Data Visualization

This document outlines security procedures, policies, and critical considerations for the Data Visualization module. While primarily focused on generating image files from provided data, several aspects require careful security attention, especially concerning input data handling, output path management, and resource consumption.

1. Introduction​

The Data Visualization module generates plots (e.g., line charts, heatmaps) from numerical and categorical data using libraries like Matplotlib and Seaborn. Its security relies on the robustness of these underlying libraries and careful handling of inputs and outputs by this module.

2. Core Security Principles​

  • Input Validation: Data and parameters provided to plotting functions should be validated for type, structure, and reasonable bounds where applicable.
  • Secure Output Handling: Ensure that file output operations are restricted and do not allow arbitrary file writes.
  • Resource Management: Be mindful of inputs that could lead to excessive resource consumption during plot generation.
  • Dependency Security: Rely on up-to-date and secure versions of underlying libraries (Matplotlib, Seaborn, NumPy).

3. Threat Model​

3.1. Malicious Input Data​

  • Resource Exhaustion (Denial of Service - DoS):
    • Extremely large datasets, or data structures designed to maximize computation (e.g., excessive number of points, categories, or very complex plot configurations) could lead to high CPU or memory usage, potentially impacting the performance of the application using this module.
    • Specially crafted numerical data (e.g., NaNs, Infs in specific places) might trigger edge cases in plotting libraries, leading to unexpected behavior or crashes, though less likely to be direct security vulnerabilities themselves.
  • Data-Driven Exploits in Dependencies (Low Risk for typical usage):
    • While Matplotlib/Seaborn are generally robust, a theoretical vulnerability in their data parsing or rendering logic, when processing highly malicious or malformed data, could exist. This is considered a low risk if libraries are kept updated.

3.2. Output Path Manipulation​

  • Arbitrary File Write/Overwrite: If the output_path parameter (in MCP tools or direct function calls) is not properly validated and sanitized, an attacker might provide a path (e.g., ../../../../etc/passwd, C:\Windows\system32\config.sam) to overwrite critical system files or write files in unintended locations, assuming the process has sufficient permissions.

3.3. Information Disclosure via Plots (Context-Dependent)​

  • Sensitive Data in Titles/Labels/Annotations: If user-controlled data is directly used in plot titles, labels, or annotations, and these plots are then displayed in a web context without proper sanitization by the consuming application, it could lead to Cross-Site Scripting (XSS). This module primarily generates static image files (e.g., PNG, JPEG) or vector graphics (e.g., SVG, PDF). The XSS risk is primarily associated with SVGs if they were to contain embedded scripts and are then rendered directly in a browser by a consuming application without appropriate sanitization or Content Security Policy (CSP). The module itself aims to produce standard, non-scripted SVG output. Consuming applications must be aware of how they handle and render any generated image or vector files, especially SVGs from potentially untrusted plot configurations.

3.4. Vulnerabilities in Dependencies​

  • Exploitation of known (or unknown) vulnerabilities in Matplotlib, Seaborn, NumPy, or the Python runtime itself. Mitigation relies on keeping these dependencies updated.

4. Key Security Measures & Mitigations​

  • Input Validation & Sanitization:
    • For MCP tools, string inputs for labels, titles, etc., should be treated as potentially untrusted. While this module primarily generates static images, if these strings are ever used in contexts that might interpret HTML/JS (e.g., SVG title tags rendered in a browser), the calling application should sanitize them.
    • Numerical data inputs (data, x_data, y_data, values, sizes) are generally passed to robust libraries. The module should perform basic type checks.
    • Consider adding size limits or complexity checks on input data arrays if resource exhaustion is a concern for specific use cases (e.g., capping the number of data points or categories).
  • Secure File Output (output_path):
    • Path Validation: The application or MCP server using this module MUST validate and sanitize output_path. This is the most critical aspect for this module's secure operation when file output is enabled.
    • Restricted Base Directory: Ideally, all plot outputs should be written to a pre-defined, non-critical base directory, configured securely at the application or MCP server level. Any output_path provided to the plotting functions (especially via MCP tools) should be programmatically confirmed to be within this restricted base directory, and the filename itself should be sanitized (e.g., to prevent overly long names, invalid characters).
    • Path Traversal Prevention: Ensure output_path cannot contain path traversal sequences (e.g., ../). Normalize paths (e.g., using os.path.abspath or pathlib.Path.resolve()) and verify they resolve to the intended secure location within the allowed base directory.
    • Permissions: The process running the plotting functions should have minimal necessary file system write permissions, restricted only to the designated output directories.
  • Dependency Management:
    • Keep Matplotlib, Seaborn, NumPy, and Python itself updated to their latest secure versions. Monitor their vulnerability disclosures.
    • Use pinned versions in pyproject.toml.
  • Resource Limiting (Application Level):
    • If the application calling this module anticipates very large or complex plot requests, it may need to implement its own timeout or resource monitoring around calls to the plotting functions, or run them in a separate, resource-constrained process/thread.
  • Logging: Use the logging_monitoring module for logging. Ensure logs do not inadvertently record excessively large data payloads if an error occurs during processing of such data. Sensitive information from data should not be logged directly.

5. Reporting a Vulnerability​

If you discover a security vulnerability within this module, please report it to us as soon as possible. We take all security reports seriously.

DO NOT report security vulnerabilities through public GitHub issues.

Instead, please email blanket@activeinference.institute with the subject line: "SECURITY Vulnerability Report: Data Visualization - Security Issue".

Please include the following information in your report:

  • A detailed description of the vulnerability and its potential impact.
  • Clear, step-by-step instructions to reproduce the vulnerability, including any specific data, configurations, or MCP requests required.
  • Any proof-of-concept (PoC) code or data.
  • The version(s) of the module affected (if known, or commit hash).
  • Your name and contact information (optional, but helpful for follow-up).

We aim to acknowledge receipt of your vulnerability report within 1-2 business days and will work with you to understand and remediate the issue. We may request additional information if needed. Public disclosure of the vulnerability will be coordinated with you after the vulnerability has been fixed and an update is available, or after a reasonable period if a fix is not immediately possible.

6. Security Updates​

Security patches and updates for this module will be documented in the module changelog and released as part of regular version updates. Critical vulnerabilities may warrant out-of-band releases. Users are strongly encouraged to keep the module updated to the latest stable version.

7. Scope​

This security policy applies only to the Data Visualization module within the Codomyrmex project. For project-wide security concerns, please refer to the main project's security policy (if one exists) or contact the core project maintainers.

8. Best Practices for Using This Module Securely​

  • Always use the latest stable version of this module and its dependencies.
  • Validate and Sanitize output_path: If you are building an application that takes output_path from an external source (e.g., user input, API request), rigorously validate it to prevent directory traversal and ensure writes are confined to designated, safe directories.
  • Be Mindful of Data Sources: If data for plots (including labels and titles) comes from untrusted external sources, be aware of potential resource exhaustion or, if rendered in a web context by your application, the need for XSS sanitization of textual elements by your application.
  • Monitor Resource Usage: If generating a large number of plots or plots from very large datasets, monitor the resource consumption of the application.
  • Least Privilege: Run the application utilizing this module with the minimum necessary permissions, especially file system permissions related to output_path.

Thank you for helping keep Codomyrmex and the Data Visualization module secure.