threat-modeling.md

Threat Model Analysis for lllyasviel/fooocus

Threat: Malicious Model Substitution (via File System)

• Description: An attacker with local file system access (or through a separate vulnerability allowing file uploads) replaces a legitimate model checkpoint file (e.g., .safetensors, .ckpt) loaded by Fooocus with a tampered or malicious version. The attacker could craft the malicious model to generate harmful content, exfiltrate data, or potentially exploit vulnerabilities in model parsing libraries used within Fooocus.
  • Impact:
    • Generation of inappropriate, illegal, or harmful content.
    • Potential for data exfiltration (steganography within generated images).
    • Possible remote code execution (RCE) if vulnerabilities in model parsing libraries within Fooocus or its direct dependencies are exploited.
    • Reputational damage to the application and its operators.
  • Fooocus Component Affected:
    • model_manager.py (specifically, functions related to loading models from disk, like load_model_from_file).
    • The overall model loading pipeline within Fooocus, including any functions that handle file paths and model selection.
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Strict File Permissions: Ensure that the directory containing model files has the most restrictive permissions possible. Only the Fooocus process should have read access, and no users should have write access.
    • Model Checksum Verification: Before loading any model, Fooocus should calculate its cryptographic hash (SHA-256) and compare it to a known-good value stored securely (e.g., in a separate, read-only configuration file or database). Fooocus should reject the model if the hashes don't match.
    • Digital Signatures: Implement a system for digitally signing models from trusted sources. Fooocus should verify the signature before loading.
    • Sandboxing: Load and process models within Fooocus in a sandboxed environment (e.g., a container with limited privileges and resource access) to contain any potential exploits. This sandboxing should be part of the Fooocus execution environment.
    • Regular Audits: Periodically audit the model files on disk to ensure their integrity.

Threat: Malicious Model Substitution (via URL)

• Description: If Fooocus is configured to download models from URLs, an attacker could manipulate a Fooocus configuration file or intercept network traffic (e.g., through a Man-in-the-Middle attack) to redirect Fooocus to a malicious model URL.
  • Impact: Same as above (Malicious Model Substitution via File System).
  • Fooocus Component Affected:
    • model_manager.py (functions related to downloading models, like those using requests or similar libraries within Fooocus).
    • Configuration parsing logic within Fooocus (e.g., functions that read model URLs from config.txt or similar).
  • Risk Severity: Critical
  • Mitigation Strategies:
    • HTTPS Enforcement: Fooocus must always use HTTPS for downloading models. Do not allow HTTP connections.
    • Certificate Pinning: Implement certificate pinning within Fooocus to ensure that it only connects to servers with specific, trusted certificates, preventing MITM attacks.
    • URL Whitelist: Fooocus should maintain a whitelist of trusted model download URLs. Reject any requests to URLs not on the whitelist.
    • Checksum Verification (Post-Download): After Fooocus downloads a model, it should immediately calculate its checksum and verify it against a known-good value.
    • Sandboxing: As above (sandboxing within the Fooocus execution context).

Threat: Input Parameter Manipulation (Prompt Injection) - Focusing on Fooocus-Specific Aspects

• Description: An attacker crafts malicious input prompts specifically designed to trigger unexpected behavior within Fooocus's prompt parsing and processing logic, or to exploit vulnerabilities in how Fooocus interacts with its core image generation libraries (e.g., diffusers). This goes beyond general input validation and targets Fooocus's specific implementation.
  • Impact:
    • Denial of Service (DoS) due to excessive resource consumption triggered by Fooocus's handling of the prompt.
    • Information disclosure (e.g., revealing details about Fooocus's internal model configuration).
    • Generation of unexpected or undesirable content due to flaws in Fooocus's prompt handling.
    • Potential for triggering vulnerabilities in underlying libraries through Fooocus's specific API calls.
  • Fooocus Component Affected:
    • process_images.py (functions that handle user input, parse prompts, and apply styles within Fooocus).
    • Any functions within Fooocus that interact with the diffusers library based on user input.
    • Potentially, style-related modules within Fooocus if custom styles are supported and not properly sanitized by Fooocus.
  • Risk Severity: High
  • Mitigation Strategies:
    • Fooocus-Specific Input Validation: Implement strict input validation within Fooocus for all parameters, including prompts, styles, seeds, and other settings. Define allowed character sets, lengths, and formats based on Fooocus's expected input.
    • Fooocus-Specific Input Sanitization: Sanitize user input within Fooocus to remove or escape any potentially harmful characters or sequences that could exploit Fooocus's parsing logic.
    • Rate Limiting (within Fooocus): If Fooocus handles requests directly, limit the rate at which it processes requests.
    • Resource Quotas (within Fooocus): If Fooocus manages resources, set limits on resource consumption (CPU, GPU, memory) per request.
    • Regular Expression Filtering (Fooocus-Specific): Use carefully crafted regular expressions within Fooocus to filter out known malicious prompt patterns that target Fooocus's implementation.

Threat: Configuration File Tampering

• Description: An attacker gains access to Fooocus's configuration files (e.g., config.txt, or custom configuration files used by Fooocus) and modifies them to alter Fooocus's behavior. This could include changing model paths, enabling debug modes, or modifying security settings within Fooocus.
  • Impact:
    • Loading of malicious models by Fooocus.
    • Exposure of sensitive information (if debug mode is enabled in Fooocus).
    • Disabling of security features within Fooocus.
    • Alteration of Fooocus's behavior in unpredictable ways.
  • Fooocus Component Affected:
    • Any code within Fooocus that reads and parses configuration files. This is likely spread across multiple modules.
  • Risk Severity: High
  • Mitigation Strategies:
    • Secure File Permissions: Restrict access to configuration files used by Fooocus using appropriate file system permissions.
    • Integrity Checks: Fooocus should calculate checksums of its configuration files and verify them on startup.
    • Configuration Management: Use a configuration management system (e.g., Ansible, Chef, Puppet) to enforce desired configurations and detect unauthorized changes to files used by Fooocus.
    • Read-Only Configuration: If possible, make the configuration files read-only for the Fooocus process after initial setup.

Threat: Dependency Vulnerabilities (Supply Chain Attack) - Direct Fooocus Dependencies

• Description: A vulnerability is discovered in one of Fooocus's direct dependencies (e.g., a specific version of diffusers, transformers, or other Python packages listed in Fooocus's requirements.txt). An attacker exploits this vulnerability to compromise Fooocus. This focuses on vulnerabilities in packages directly used by Fooocus, not general system libraries.
  • Impact:
    • Remote Code Execution (RCE) within the Fooocus process.
    • Denial of Service (DoS) of Fooocus.
    • Information Disclosure from Fooocus.
    • Data Corruption within Fooocus.
  • Fooocus Component Affected:
    • Potentially any part of Fooocus that uses the vulnerable dependency.
  • Risk Severity: High to Critical (depending on the vulnerability)
  • Mitigation Strategies:
    • Dependency Management: Use a requirements.txt file for Fooocus and pin dependencies to specific, known-good versions.
    • Vulnerability Scanning: Regularly scan Fooocus's dependencies for known vulnerabilities using tools like pip-audit, safety, or dedicated Software Composition Analysis (SCA) tools.
    • Virtual Environments: Use virtual environments to isolate Fooocus's dependencies and prevent conflicts.
    • Prompt Updates: Update Fooocus's dependencies promptly when security patches are released.
    • Dependency Monitoring: Continuously monitor for new vulnerability disclosures related to Fooocus's direct dependencies.