threat-modeling.md

Threat Model Analysis for keras-team/keras

Threat: Malicious Model Deserialization

• Description: An attacker crafts a malicious Keras model file (e.g., HDF5, pickle) containing embedded code. When the application loads this model using Keras model loading functions (e.g., keras.models.load_model), the malicious code is executed on the server. This can lead to arbitrary code execution, allowing the attacker to gain full control of the server, steal data, or disrupt operations.
• Impact: Critical. Full server compromise, data breach, denial of service, reputational damage.
• Affected Keras Component: keras.models.load_model, potentially backend serialization/deserialization functions (TensorFlow, etc.).
• Risk Severity: Critical if loading models from untrusted sources is possible.
• Mitigation Strategies:
  • Load models only from trusted and verified sources. Prefer pre-trained models stored securely within your infrastructure.
  • Implement strict input validation on file paths or URLs used for model loading, but this is insufficient to prevent malicious content within the file itself.
  • Consider model signing and integrity checks to verify model authenticity before loading.
  • Run model loading and inference in sandboxed environments with restricted privileges.
  • Regularly update Keras and backend libraries to patch known deserialization vulnerabilities.

Threat: Backend Dependency Vulnerability Exploitation

• Description: Keras relies on backend libraries like TensorFlow. Attackers exploit known vulnerabilities in these backend libraries (e.g., in TensorFlow's graph execution engine, parsing libraries, or CUDA drivers) through Keras. This could involve crafting specific model architectures, input data, or operations that trigger the backend vulnerability when processed by Keras.
• Impact: High to Critical. Depending on the backend vulnerability, impacts can range from denial of service, information disclosure, to arbitrary code execution on the server or even the GPU.
• Affected Keras Component: Keras core functionalities that rely on the backend, including layers, models, optimizers, loss functions, and data handling. Ultimately, the vulnerability lies in the backend library (e.g., TensorFlow).
• Risk Severity: High if using older versions of backend libraries or not regularly patching.
• Mitigation Strategies:
  • Keep backend libraries (TensorFlow, etc.) updated to the latest stable versions. Monitor security advisories and patch releases.
  • Follow security best practices recommended by backend library providers.
  • Use security scanning tools to identify known vulnerabilities in backend dependencies.
  • Isolate Keras application and backend dependencies from other critical systems.

Threat: Data Poisoning during Training

• Description: An attacker with control over the training data can inject malicious or manipulated data into the training dataset. This poisoned data can influence the model's learning process, causing it to make incorrect predictions or exhibit biased behavior in a way that benefits the attacker. This is particularly relevant if training data is sourced from user-generated content or external, less trusted sources.
• Impact: Medium to High. Model accuracy degradation, biased predictions, model manipulation for attacker's benefit, reputational damage if the model's flawed behavior is publicly visible.
• Affected Keras Component: Keras training process (model.fit, model.train_step), data loading and preprocessing pipelines.
• Risk Severity: High if training data sources include user-generated content or data from less trusted external sources.
• Mitigation Strategies:
  • Implement strict data validation and sanitization for all training data sources.
  • Monitor the training process for anomalies that might indicate data poisoning (e.g., sudden drops in accuracy, unusual loss values).
  • Use robust training techniques less susceptible to data poisoning (e.g., anomaly detection, outlier removal, robust statistics).
  • Control access to training data and the training process.
  • Consider data augmentation techniques to improve model robustness against small amounts of poisoned data.

Threat: Adversarial Input Exploitation

• Description: An attacker crafts adversarial examples – inputs subtly modified to intentionally mislead the Keras model into making incorrect predictions. These modifications are often imperceptible to humans but can drastically alter the model's output. This can be used to bypass security measures, manipulate decision-making processes, or cause denial of service by forcing the model to process computationally expensive adversarial inputs.
• Impact: Medium to High. Bypassing security controls (e.g., in image recognition-based authentication), incorrect decisions based on model predictions, denial of service through computationally expensive adversarial examples.
• Affected Keras Component: Keras model inference (model.predict, model.call), input preprocessing layers.
• Risk Severity: High if the application is used for security-sensitive tasks or decision-making with significant consequences.
• Mitigation Strategies:
  • Implement input validation and sanitization to detect and potentially block some adversarial inputs, but sophisticated examples are hard to detect.
  • Consider adversarial training to make models more robust against adversarial attacks.
  • Monitor model predictions for anomalies and unexpected behavior.
  • Implement rate limiting and input throttling to mitigate brute-force attempts to find adversarial examples.
  • Explore defensive distillation or other defense mechanisms (effectiveness varies and is an active research area).

Threat: Resource Exhaustion via Inference Requests

• Description: An attacker floods the Keras application with a large volume of inference requests, or crafts requests with computationally expensive inputs. This can overwhelm the server's resources (CPU, memory, GPU), leading to denial of service for legitimate users. Specifically crafted inputs could exploit inefficient model architectures or operations, amplifying resource consumption.
• Impact: Medium to High. Denial of service, application unavailability, performance degradation for legitimate users, increased infrastructure costs due to resource over-utilization.
• Affected Keras Component: Keras model inference (model.predict, model.call), application server infrastructure hosting the Keras model.
• Risk Severity: High if the application is business-critical and requires high availability.
• Mitigation Strategies:
  • Implement rate limiting and input throttling to control the number of inference requests.
  • Set resource limits (CPU, memory, GPU) for model inference processes.
  • Monitor resource usage and application performance to detect and respond to potential DoS attacks.
  • Use caching mechanisms to reduce computational load for frequent requests.
  • Employ load balancing to distribute inference requests across multiple servers.
  • Optimize model architecture and inference code for efficiency to reduce resource consumption per request.