threat-modeling.md

Threat Model Analysis for nvlabs/stylegan

Threat: Malicious Model Substitution

• Description: An attacker replaces the legitimate StyleGAN .pkl (or other model format) file with a crafted malicious version. Access could be gained via compromised server infrastructure, supply chain attacks, or social engineering. The malicious model could generate biased, offensive, privacy-violating outputs, or cause resource exhaustion.
• Impact:
  • Generation of inappropriate/harmful content.
  • Reputational damage.
  • Privacy violations (if the malicious model leaks training data).
  • Denial-of-service (resource exhaustion).
  • Potential legal liability.
• Affected Component:
  • dnnlib.tflib.Network (or equivalent for model loading). The function that deserializes the model.
  • The model file itself (.pkl or other).
• Risk Severity: Critical
• Mitigation Strategies:
  • Cryptographic Hashing: Calculate a strong hash (SHA-256+) of the legitimate model. Store this hash securely and separately. Verify before loading.
  • Secure Model Storage: Restricted access, ACLs, dedicated secure model repository.
  • Digital Signatures: Sign the model file; verify the signature before loading.
  • Regular Audits: Re-verify the hash, review access logs.

Threat: Input Manipulation (Latent Vector/Parameter Tampering)

• Description: An attacker manipulates numerical inputs to StyleGAN (latent vectors, psi, noise, style mixing) to force undesirable outputs. They might find "sensitive regions" in the latent space. This is not about general adversarial examples, but targeted manipulation.
• Impact:
  • Generation of targeted inappropriate content.
  • Circumvention of output filters.
  • Amplification of subtle biases.
• Affected Component:
  • run_generator.py (or equivalent for input handling).
  • Gs.run() (or equivalent) that takes the latent vector, etc.
• Risk Severity: High
• Mitigation Strategies:
  • Strict Input Validation: Rigorous validation for all numerical inputs. Enforce ranges, data types, allowed values.
  • Input Sanitization: Remove potentially harmful characters/patterns.
  • Latent Space Exploration/Monitoring: Identify "sensitive regions" and monitor for inputs targeting them.
  • Randomization: Add controlled randomness to inputs.

Threat: Training Data Leakage (Model Inversion)

• Description: An attacker crafts inputs to elicit outputs revealing information about the training data (model inversion). They might try to reconstruct faces or other sensitive data.
• Impact:
  • Privacy violations (if training data contains PII).
  • Exposure of confidential data.
  • Reputational damage.
• Affected Component:
  • The trained StyleGAN model (.pkl file - learned weights/biases).
  • Gs.run() (or equivalent) - the inference function.
• Risk Severity: High
• Mitigation Strategies:
  • Differential Privacy (During Training): Use DP techniques during training. This is the most effective mitigation.
  • Careful Training Data Selection: Avoid PII. Use strong anonymization/pseudonymization if PII is unavoidable.
  • Output Filtering: Detect/block outputs resembling sensitive data/training examples (reactive, less reliable).
  • Membership Inference Attack Testing: Assess vulnerability to revealing training data membership.

Threat: Denial of Service (Resource Exhaustion)

• Description: Attacker sends many requests or crafts malicious inputs (large latent vectors, unusual parameters) to consume excessive CPU/GPU/memory, causing a DoS.
• Impact:
  • Application unavailability.
  • Increased costs (cloud resources).
  • Potential cascading failures.
• Affected Component:
  • run_generator.py (or equivalent) - inference request entry point.
  • Gs.run() (or equivalent) - inference function.
  • The entire StyleGAN model and libraries (TensorFlow, etc.).
• Risk Severity: High
• Mitigation Strategies:
  • Rate Limiting: Limit requests per IP/user.
  • Resource Limits: Limit CPU/GPU time, memory per request.
  • Input Validation: (As in Threat 2) - reject bad inputs.
  • Timeouts: Prevent requests from running indefinitely.
  • Load Balancing: Distribute requests across servers.
  • Model Optimization: Optimize for inference speed/efficiency.

Threat: Code Modification (Tampering with StyleGAN Code)

• Description: An attacker gains access to and modifies the StyleGAN source code (original NVIDIA or your modifications) to introduce malicious behavior, backdoors, or vulnerabilities.
• Impact:
  • Complete compromise of the StyleGAN component.
  • Potential for arbitrary code execution.
  • All other impacts (depending on modification).
• Affected Component:
  • All StyleGAN source code (.py files, etc.).
  • Custom code interacting with StyleGAN.
• Risk Severity: Critical
• Mitigation Strategies:
  • Source Code Control/Versioning: Strict access controls, strong authentication.
  • Code Reviews: Mandatory reviews for all changes.
  • Static Analysis: Scan for vulnerabilities.
  • Dependency Management: Manage, pin, and audit dependencies.
  • Integrity Checks: Calculate and verify hashes of source files.

Threat: Exploitation of Implementation Vulnerabilities

• Description: An attacker exploits a previously unknown vulnerability in the StyleGAN code (or dependencies like TensorFlow) for unauthorized access/control. Less likely with a well-vetted library, but possible.
• Impact:
  • Potentially arbitrary code execution.
  • Complete system compromise.
  • Data breaches.
• Affected Component:
  • Potentially any part of StyleGAN or dependencies.
• Risk Severity: Critical
• Mitigation Strategies:
  • Keep Software Updated: Update StyleGAN, TensorFlow, all dependencies.
  • Security Audits: Regular audits, penetration testing.
  • Sandboxing: Run inference in a sandboxed environment.
  • Principle of Least Privilege: Minimum necessary privileges.
  • Vulnerability Scanning: Detect known vulnerabilities.