threat-modeling.md

Threat Model Analysis for fluxml/flux.jl

Threat: Model Poisoning via Malicious Training Data

• Description: An attacker uploads a crafted dataset containing subtly altered or incorrect data points. The attacker's goal is to manipulate the training process, causing the trained model to produce incorrect predictions, favor specific outcomes, or exhibit biased behavior. The attacker might add noise, mislabel data, or introduce adversarial examples.
• Impact: The trained model becomes unreliable, leading to incorrect predictions, potentially causing financial losses, reputational damage, or unfair outcomes. The integrity of the entire system is compromised.
• Affected Flux.jl Component: Primarily affects the Flux.train! function and any custom training loops that use Flux.Optimise.update!. The model architecture itself (e.g., Chain, Dense, convolutional layers) is indirectly affected as it learns from the poisoned data. Loss functions (e.g., Flux.Losses.mse, Flux.Losses.crossentropy) are also involved, as they guide the training process.
• Risk Severity: Critical
• Mitigation Strategies:
  • Rigorous Data Validation: Implement strict checks on uploaded data, including data type validation, range checks, outlier detection, and statistical analysis to identify anomalies. Consider using schema validation libraries.
  • Data Sanitization: Remove or transform potentially malicious data points. This might involve techniques like data normalization, standardization, or robust statistical methods.
  • Adversarial Training: Train the model with adversarial examples (intentionally perturbed inputs) to make it more robust to malicious data. Use libraries like Adversarial.jl (if compatible) or implement custom adversarial training routines.
  • Differential Privacy: Add noise to the training process to protect the privacy of individual data points and make the model less sensitive to small changes in the input data.
  • Data Provenance: Track the origin and history of training data to ensure its integrity.
  • Regular Retraining: Periodically retrain the model on a verified, clean dataset.
  • Model Monitoring: Continuously monitor the model's performance for unexpected deviations that might indicate poisoning.

Threat: Model Parameter Tampering via Direct File Modification

• Description: An attacker gains unauthorized access to the server's file system or the storage location where the trained model is saved (e.g., a .bson file). The attacker directly modifies the model's parameters (weights, biases) or architecture using a text editor or other tools.
• Impact: The model's behavior becomes unpredictable and potentially malicious. The attacker can control the model's output, leading to arbitrary results.
• Affected Flux.jl Component: Affects the saved model file (typically .bson when using Flux.loadmodel!). The loading process itself (Flux.loadmodel!) is the point of vulnerability.
• Risk Severity: Critical
• Mitigation Strategies:
  • Secure Storage: Store model files in a secure location with strict access controls (e.g., AWS S3 with IAM roles, encrypted file systems).
  • File Permissions: Set appropriate file permissions to prevent unauthorized access and modification.
  • Integrity Checks: Calculate a cryptographic hash (e.g., SHA-256) of the model file before saving it. Before loading the model, recalculate the hash and compare it to the stored value. If the hashes don't match, the file has been tampered with.
  • Version Control: Use a version control system (e.g., Git) to track changes to model files and allow rollback to previous versions.
  • Code Signing: Digitally sign the model file to ensure its authenticity and integrity.

Threat: Gradient Manipulation during Training (Adversarial Attack)

• Description: An attacker exploits vulnerabilities in custom loss functions, optimizers, or the training loop itself to inject malicious gradients during the training process. This is a more sophisticated attack than data poisoning and requires a deeper understanding of the training process.
• Impact: The trained model becomes compromised, exhibiting incorrect or biased behavior, similar to data poisoning.
• Affected Flux.jl Component: Affects Flux.Optimise.update!, custom optimizers, custom loss functions, and the overall training loop logic. The Zygote.gradient function (used for automatic differentiation) could be a target if vulnerabilities exist.
• Risk Severity: High
• Mitigation Strategies:
  • Use Well-Vetted Components: Prefer established optimizers (e.g., Flux.Optimise.ADAM, Flux.Optimise.Descent) and loss functions from the Flux ecosystem.
  • Audit Custom Components: Thoroughly review and test any custom optimizers, loss functions, or training loop modifications for vulnerabilities.
  • Gradient Clipping: Limit the magnitude of gradients during training to prevent excessively large updates that could be caused by malicious manipulation. Use Flux.clipnorm! or similar functions.
  • Adversarial Training: Train the model with adversarial examples generated during the training process to make it more robust to gradient-based attacks.

Threat: Model Inversion/Extraction via Repeated Queries

• Description: An attacker repeatedly queries the model with carefully crafted inputs, observing the outputs to reconstruct the training data or infer sensitive information about it.
• Impact: Loss of privacy of the training data. Sensitive information (e.g., personal data, medical records) could be exposed.
• Affected Flux.jl Component: Affects the model's inference process (e.g., calling the model directly like model(input)). The model architecture itself is indirectly affected, as certain architectures might be more vulnerable to model inversion.
• Risk Severity: High
• Mitigation Strategies:
  • Differential Privacy: Train the model with differential privacy techniques to add noise to the model's parameters, making it harder to extract information about individual data points.
  • Rate Limiting: Limit the number of queries a user can make to the model within a given time period.
  • Query Monitoring: Monitor for unusual query patterns that might indicate a model inversion attack (e.g., many similar queries with small variations).
  • Input Perturbation: Add small amounts of noise to the model's inputs before processing them, making it harder to reconstruct the training data.

Threat: Unsafe Deserialization of Untrusted Models

• Description: The application loads a model file (e.g., a .bson file) from an untrusted source (e.g., a user upload) without proper validation. The attacker crafts a malicious model file that, when deserialized, executes arbitrary code on the server.
• Impact: Remote code execution (RCE). The attacker gains complete control over the server. This is a catastrophic security failure.
• Affected Flux.jl Component: Affects Flux.loadmodel! (and potentially BSON.load if used directly).
• Risk Severity: Critical
• Mitigation Strategies:
  • Never Deserialize Untrusted Models: This is the most important mitigation. Do not use Flux.loadmodel! or BSON.load with data from untrusted sources.
  • Model Reconstruction: If user-uploaded models are required, define a safe, restricted format (e.g., a JSON configuration specifying allowed layers and parameters) and reconstruct the model from this validated representation. Do not directly load a serialized object.
  • Sandboxing: If direct deserialization is absolutely unavoidable (it should not be), run the model loading and inference in a highly restricted, sandboxed environment with minimal privileges.
  • Input Validation (for Reconstruction): Even when reconstructing, rigorously validate the configuration data to ensure it only contains allowed operations, layers, and parameter values.

Threat: Exploiting Vulnerabilities in Flux.jl or Dependencies

• Description: A security vulnerability is discovered in Flux.jl itself or in one of its dependencies (e.g., CUDA.jl, Zygote.jl, NNlib.jl). An attacker exploits this vulnerability to gain unauthorized access or control.
  • Impact: Varies depending on the vulnerability, but could range from information disclosure to remote code execution.
  • Affected Flux.jl Component: Potentially any part of Flux.jl or its dependencies.
  • Risk Severity: High (potentially Critical, depending on the vulnerability)
  • Mitigation Strategies:
    • Keep Software Updated: Regularly update Flux.jl and all its dependencies to the latest versions. Use Julia's package manager (Pkg) to manage updates.
    • Vulnerability Scanning: Use a dependency vulnerability scanner to identify known vulnerabilities in your project's dependencies.
    • Security Advisories: Monitor security advisories for Julia and its packages.
    • Sandboxing: Consider running Flux.jl code in a sandboxed environment to limit the impact of potential vulnerabilities.