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Threat Model Analysis for comfyanonymous/comfyui

Threat: Malicious Custom Node Execution

  • Threat: Malicious Custom Node Execution

    • Description: An attacker installs or convinces a developer to install a malicious custom node (Python script) disguised as a legitimate extension. The attacker might upload the node to a public repository, use social engineering, or exploit a vulnerability in a node repository. The malicious node could contain code to perform various harmful actions.
    • Impact:
      • Complete system compromise (RCE) on the server running ComfyUI.
      • Data theft (user inputs, outputs, models, API keys).
      • Cryptocurrency mining.
      • Network intrusion and lateral movement.
      • Persistent backdoor installation.
    • Affected ComfyUI Component: Custom Node loading mechanism (nodes.py, node execution environment). Specifically, any function that loads and executes Python code from external sources.
    • Risk Severity: Critical
    • Mitigation Strategies:
      • Strict Node Vetting: Only install nodes from trusted, reputable sources. Manually review all source code before installation.
      • Sandboxing: Implement strong sandboxing (e.g., Docker containers with minimal privileges, restricted network access). Consider using seccomp or AppArmor profiles.
      • Dependency Auditing: Scrutinize all dependencies of custom nodes. Use tools like pip-audit to check for known vulnerabilities. Pin dependency versions.
      • Least Privilege: Run ComfyUI as a non-root user with limited file system access.
      • Resource Limits: Enforce CPU, memory, GPU, and network limits on the ComfyUI process.
      • Code Signing (Ideal): Implement a code signing mechanism for custom nodes (though this is not natively supported by ComfyUI and would require significant custom development).

Threat: Model Poisoning via Uploaded Checkpoint

  • Threat: Model Poisoning via Uploaded Checkpoint

    • Description: An attacker uploads a maliciously crafted model checkpoint file (e.g., .ckpt, .safetensors). This model might be designed to produce biased, harmful, or unexpected outputs when given specific inputs, or to leak information. The attacker might exploit a file upload vulnerability or social engineer a user into loading the poisoned model.
    • Impact:
      • Generation of offensive or harmful content.
      • Circumvention of content filters.
      • Data exfiltration through model outputs.
      • Denial of service (if the model is designed to crash).
    • Affected ComfyUI Component: Model loading functions (likely within nodes.py or related model-handling modules). Specifically, any function that deserializes and loads model weights from external files.
    • Risk Severity: High
    • Mitigation Strategies:
      • Model Provenance: Only allow loading models from trusted sources. Verify checksums/hashes against known-good values.
      • Model Scanning (Limited): Explore model scanning techniques (research area). This is not a foolproof solution.
      • Input Sanitization: Sanitize all inputs to the model, even if the model itself is trusted.
      • Output Monitoring: Monitor model outputs for anomalies.
      • Restrict Model Uploads: If possible, disable or severely restrict user model uploads. Provide a curated set of pre-vetted models.

Threat: Prompt Injection in Workflow Definition

  • Threat: Prompt Injection in Workflow Definition

    • Description: An attacker crafts a malicious prompt or workflow definition that manipulates the ComfyUI workflow execution. Instead of just influencing the output of a single model, the attacker aims to alter the flow of execution, bypassing security checks or executing unintended nodes. This could involve injecting special characters or code into node parameters or connections.
    • Impact:
      • Bypassing safety mechanisms.
      • Executing arbitrary nodes with attacker-controlled parameters.
      • Data leakage or modification.
      • Resource exhaustion.
    • Affected ComfyUI Component: Workflow parsing and execution engine (likely within execution.py and related modules). Specifically, any function that interprets and executes user-provided workflow definitions (JSON or similar).
    • Risk Severity: High
    • Mitigation Strategies:
      • Strict Workflow Validation: Validate the entire workflow structure against a whitelist of allowed configurations. Do not allow arbitrary node connections or parameter values.
      • Parameter Sanitization: Strictly sanitize and validate all user-provided parameters for each node. Use type checking and input validation.
      • Input Templating: Use a secure templating engine to construct workflows, preventing direct concatenation of user input.
      • Context-Aware Escaping: If any user input must be included in the workflow definition, use context-aware escaping to prevent injection.

Threat: Dependency Vulnerabilities (Supply Chain Attack)

  • Threat: Dependency Vulnerabilities (Supply Chain Attack)

    • Description: A vulnerability exists in one of the Python packages that ComfyUI or a custom node depends on. An attacker could exploit this vulnerability to gain control of the system. This is a "supply chain" attack because the vulnerability originates from a third-party component.
    • Impact:
      • Similar to malicious custom node execution: RCE, data theft, etc.
    • Affected ComfyUI Component: Any component that uses the vulnerable dependency.
    • Risk Severity: High
    • Mitigation Strategies:
      • Dependency Management: Use a requirements.txt or pyproject.toml file to manage dependencies.
      • Vulnerability Scanning: Regularly scan dependencies for known vulnerabilities using tools like pip-audit, safety, or Dependabot.
      • Pinning Versions: Pin dependency versions to specific, known-good releases to prevent automatic updates to vulnerable versions.
      • Virtual Environments: Use virtual environments to isolate project dependencies.