mitigations.md

Mitigation Strategies Analysis for lllyasviel/fooocus

Mitigation Strategy: Strict Model Whitelisting (Modified for Fooocus)

Description: 1. Identify Trusted Models: Determine a list of reputable sources and specific, trusted models. 2. Download and Verify: Download models and obtain/calculate their SHA-256 checksums. 3. Modify config.txt (and related files): Instead of just specifying model paths, extend the configuration to include checksums. This might require modifying the parsing logic in Fooocus to handle a new format, for example: ``` # Original (Hypothetical) model_path = "models/sd_xl_base_1.0.safetensors"

    # Modified (Hypothetical - Requires Code Changes)
    model_path = "models/sd_xl_base_1.0.safetensors"
    model_checksum = "sha256:e14a996815d7999f..."  # The actual checksum
    ```
4.  **Implement Checksum Verification in Fooocus Code:** Modify the Python code in Fooocus that loads models (likely in files related to model initialization or loading).  Add logic to:
    *   Read the `model_path` *and* `model_checksum` from the configuration.
    *   Calculate the SHA-256 checksum of the file at `model_path`.
    *   Compare the calculated checksum with the `model_checksum` from the configuration.
    *   *Only* load the model if the checksums match.  If they don't, raise a `ValueError` or a custom exception, and log the error *securely* (avoiding logging sensitive information).
5.  **Error Handling:** Implement robust error handling for cases where:
    *   The checksum is missing from the configuration.
    *   The checksum doesn't match.
    *   The model file is not found.
6. **Secure Configuration Loading:** Ensure the configuration file itself is loaded securely, preventing injection of malicious paths or checksums.

• Threats Mitigated:

  • Malicious Models: (Severity: Critical) - Prevents loading of models not explicitly whitelisted.
  • Model Tampering: (Severity: High) - Detects modifications to model files.

• Impact:

  • Malicious Models: Risk reduced to near zero, if the whitelist is maintained.
  • Model Tampering: High risk reduction; detects unauthorized changes.

• Currently Implemented:

  • Partially. Fooocus allows specifying model paths, but not checksums.

• Missing Implementation:

  • Checksum verification logic within the Fooocus Python code.
  • Modification of the configuration file format and parsing to include checksums.
  • Robust error handling for failed checks.

Mitigation Strategy: Input Sanitization and Validation (Within Fooocus)

Description: 1. Identify Prompt Entry Point: Locate the exact point in the Fooocus code (Python files) where user-provided prompts are received and processed before being passed to the underlying model. 2. Implement Denylist: Create a denylist of words, phrases, and regular expressions (as described previously) within a Python module or a dedicated configuration file loaded by Fooocus. 3. Add Filtering Logic: Insert code at the prompt entry point to: * Lowercase the prompt. * Iterate through the denylist. * Use string matching or regular expressions to check for matches. * If a match is found: * Option A: Raise an exception and prevent image generation. Log the event securely. * Option B: Replace the matched term with a safe alternative (e.g., replace a sensitive word with "[REDACTED]"). Log the modification. 4. Length Limits: Enforce a maximum prompt length within the Fooocus code. If the prompt exceeds the limit, raise an exception or truncate the prompt (logging the truncation). 5. Character Restrictions: Implement checks to ensure the prompt only contains allowed characters. Reject or sanitize prompts containing disallowed characters. 6. Log Sanitization: Log all sanitization actions, including the original prompt, the detected issue, and the action taken (rejection, replacement, truncation).

• Threats Mitigated:

  • Indirect Prompt Injection: (Severity: Medium) - Reduces the risk of prompts designed to elicit harmful outputs.
  • Resource Exhaustion (Partial): (Severity: Medium) - Length limits help.

• Impact:

  • Indirect Prompt Injection: Moderate risk reduction.
  • Resource Exhaustion: Some protection against overly long prompts.

• Currently Implemented:

  • No.

• Missing Implementation:

  • All sanitization and validation logic needs to be added to the Fooocus code at the prompt processing stage.
  • The denylist needs to be created and integrated.

Mitigation Strategy: Timeout Configuration (Within Fooocus)

Description: 1. Locate Inference Code: Identify the specific Python functions within Fooocus that perform the actual image generation (inference) using the loaded model. 2. Add Timeout Parameters: Modify these functions to accept a timeout parameter (in seconds or milliseconds). 3. Implement Timeout Logic: Within the inference functions, use the timeout parameter with the underlying libraries (e.g., PyTorch, transformers) to set a time limit for the generation process. This usually involves passing the timeout to the relevant function calls. 4. Handle Timeout Exceptions: Wrap the inference code in a try...except block to catch timeout exceptions (e.g., torch.cuda.TimeoutError if using PyTorch with CUDA). 5. Error Handling: When a timeout exception occurs: * Log the event securely. * Raise a custom exception (e.g., FooocusTimeoutError) that can be handled by the calling code. 6. Default Timeout: Set a reasonable default timeout value in the configuration (e.g., config.txt) if one is not provided. 7. Expose Timeout in Configuration: Allow users to configure the timeout value through the config.txt file (or a similar mechanism).

• Threats Mitigated:

  • Resource Exhaustion: (Severity: Medium) - Prevents excessively long generation times.
  • Denial of Service (DoS) (Partial): (Severity: High) - Helps mitigate DoS attacks that rely on slow requests.

• Impact:

  • Resource Exhaustion: Reduces the risk of resource exhaustion.
  • DoS: Provides some protection.

• Currently Implemented:

  • No.

• Missing Implementation:

  • Timeout parameters and logic need to be added to the inference functions within Fooocus.
  • Exception handling for timeouts needs to be implemented.
  • Configuration options for timeouts need to be added.

Mitigation Strategy: Dependency Management (Within Fooocus)

Description: 1. Review requirements.txt or pyproject.toml: Carefully examine the file that lists Fooocus's dependencies (likely requirements.txt or pyproject.toml). 2. Pin Exact Versions: Specify exact versions for all dependencies, including transitive dependencies. Use a tool like pip freeze to generate a requirements file with pinned versions after testing a known-good configuration. Example: ``` # Instead of: torch transformers

    # Use:
    torch==2.0.1
    transformers==4.30.2
    # ... and so on for ALL dependencies
    ```
3.  **Justify Each Dependency:**  Ensure that every dependency is *absolutely necessary*.  Remove any unused or optional dependencies.
4.  **Regular Updates (with Testing):**  Establish a process for regularly updating dependencies.  This involves:
    *   Updating the pinned versions in the requirements file.
    *   Running *thorough* tests to ensure that the updates don't introduce any regressions or compatibility issues.
    *   *Only* deploying the updated dependencies after successful testing.
5. **Consider a Separate Virtual Environment:** Use a virtual environment (e.g., `venv` or `conda`) to isolate Fooocus's dependencies from other Python projects on the system. This is a general best practice, but it's particularly important for managing dependencies.

• Threats Mitigated:

  • Vulnerable Dependencies: (Severity: High) - Reduces the risk of using dependencies with known vulnerabilities.
  • Supply Chain Attacks (Partial): (Severity: High) - Pinning versions makes it harder for an attacker to inject a malicious dependency through an unexpected update.

• Impact:

  • Vulnerable Dependencies: Significantly reduces risk by controlling the versions used.
  • Supply Chain Attacks: Provides some protection.

• Currently Implemented:

  • Partially. Fooocus likely has a requirements.txt or similar file, but it may not have strictly pinned versions for all dependencies.

• Missing Implementation:

  • Ensure all dependencies are pinned to exact versions.
  • Establish a rigorous process for updating and testing dependencies.