threat-modeling.md

Threat Model Analysis for fluxml/flux.jl

Threat: Adversarial Attacks on Models

• Description: An attacker crafts malicious input data designed to exploit vulnerabilities in the trained Flux.jl model. They might manipulate input features in a way that causes the model to misclassify data, make incorrect predictions, or bypass security mechanisms. This could be done by subtly altering data fed into the model during inference.
• Impact: Incorrect application behavior, circumvention of security controls, business logic failures, manipulated outputs leading to significant damage.
• Flux.jl Component Affected: Model Inference (specifically the trained model and its architecture).
• Risk Severity: High
• Mitigation Strategies:
  • Implement robust input validation and sanitization before feeding data to the model.
  • Employ adversarial training techniques to make models more resilient to adversarial examples using Flux.jl's training capabilities.
  • Monitor model performance for unexpected deviations and anomalies in output during inference.
  • Consider using defensive distillation or ensemble methods (implementable with Flux.jl) to enhance model robustness.

Threat: Model Backdoor Attacks

• Description: An attacker subtly manipulates the model during the training phase (potentially using Flux.jl's training functionalities) to introduce a hidden "backdoor". This backdoor is triggered by specific, attacker-chosen input patterns. When these trigger patterns are present in the input data, the model will deviate from its intended behavior and perform actions dictated by the attacker, while behaving normally for other inputs.
• Impact: Circumvention of security controls under specific conditions, targeted manipulation of application behavior, subtle and hard-to-detect attacks that can be activated at the attacker's discretion, leading to significant security breaches.
• Flux.jl Component Affected: Model Training Pipeline (specifically the training process and model architecture built with Flux.jl).
• Risk Severity: High
• Mitigation Strategies:
  • Conduct rigorous code reviews of the training pipeline and model architecture, paying close attention to custom layers or loss functions defined using Flux.jl.
  • Verify the integrity and provenance of training data and training scripts used with Flux.jl.
  • Perform extensive model testing and validation against a wide range of inputs, including potentially malicious or unusual patterns, using Flux.jl for testing and evaluation.
  • Explore neural network verification techniques to detect potential backdoors in trained models built with Flux.jl.

Threat: Data Injection in Preprocessing or Postprocessing Steps

• Description: An attacker injects malicious code or data into the data preprocessing or postprocessing steps of the ML pipeline if these steps are implemented using Flux.jl or Julia code. If user-supplied data is directly used in these steps without proper sanitization within Flux.jl workflows, an attacker could inject commands or scripts that are then executed by the application during data processing. This could involve exploiting vulnerabilities in data manipulation functions used in conjunction with Flux.jl.
• Impact: Code execution on the server, data leakage, manipulation of model input or output, application crashes, potential for privilege escalation depending on the context of execution within the Flux.jl application.
• Flux.jl Component Affected: Data Pipeline (steps before and after Flux.jl model inference, specifically if implemented using Flux.jl or Julia code).
• Risk Severity: Critical
• Mitigation Strategies:
  • Treat all user-supplied data as untrusted and implement strict input validation and sanitization at every stage of the data pipeline within Flux.jl and surrounding Julia code.
  • Avoid using dynamic code execution functions like eval() on user-provided data within Flux.jl data pipelines.
  • Use parameterized queries or prepared statements when interacting with databases or external systems in data processing steps implemented in Julia and used with Flux.jl to prevent injection attacks.
  • Isolate data processing steps in sandboxed environments to limit the impact of potential vulnerabilities in Julia code used with Flux.jl.

Threat: Vulnerabilities in Flux.jl Dependencies

• Description: Flux.jl relies on a number of other Julia packages. High severity vulnerabilities in these dependencies (e.g., in packages for linear algebra, optimization, or data handling) could be exploited by an attacker to compromise the application. Attackers could leverage known high severity vulnerabilities in these dependencies to gain unauthorized access, execute code, or cause denial of service in applications using Flux.jl.
• Impact: Code execution, denial of service, data breaches, privilege escalation, due to vulnerabilities in Flux.jl's dependencies, leading to significant security breaches.
• Flux.jl Component Affected: Flux.jl Dependencies (external Julia packages used by Flux.jl).
• Risk Severity: High
• Mitigation Strategies:
  • Regularly update Flux.jl and all its dependencies to the latest versions to patch known vulnerabilities.
  • Utilize dependency scanning tools to automatically identify known vulnerabilities in project dependencies of Flux.jl applications.
  • Monitor security advisories and vulnerability databases for Flux.jl and its ecosystem, including its dependencies.
  • Employ a Julia environment management tool (like Pkg) to manage dependencies and ensure consistent and secure environments for Flux.jl applications.

Threat: Model Deserialization Vulnerabilities

• Description: If the application loads pre-trained Flux.jl models from external or untrusted sources (e.g., user uploads, network downloads), vulnerabilities in the model deserialization process within Flux.jl could be exploited. An attacker could craft a malicious model file that, when loaded by the application using Flux.jl's model loading functions, triggers code execution or other harmful actions.
• Impact: Remote code execution on the server or client-side, depending on where model deserialization occurs, system compromise, data breaches, resulting from vulnerabilities in Flux.jl's model loading mechanisms.
• Flux.jl Component Affected: Model Serialization/Deserialization (functions used to save and load Flux.jl models, potentially related to BSON or custom serialization methods within Flux.jl).
• Risk Severity: Critical
• Mitigation Strategies:
  • Only load models from trusted and verified sources. Avoid loading models from untrusted user uploads or public repositories without thorough vetting.
  • Implement integrity checks (e.g., cryptographic signatures) for models loaded from external sources to verify their authenticity and prevent tampering before loading them with Flux.jl.
  • Carefully review the Flux.jl model serialization and deserialization mechanisms for potential vulnerabilities. If using custom serialization with Flux.jl, ensure it is secure.
  • Consider sandboxing or isolating the model deserialization process in a restricted environment to limit the impact of potential exploits when loading models into Flux.jl applications.

Threat: Exploiting Custom Layers or Functions

• Description: If the application allows users to define or provide custom layers or functions that are then incorporated into Flux.jl models, this can create an injection point for malicious code. An attacker could provide malicious code disguised as a custom layer or function that is executed when the model is built or during inference within Flux.jl.
• Impact: Code execution, privilege escalation, manipulation of model behavior, system compromise, due to execution of malicious custom layers or functions within Flux.jl.
• Flux.jl Component Affected: Custom Layers/Functions (feature in Flux.jl allowing user-defined components within models).
• Risk Severity: High
• Mitigation Strategies:
  • Avoid allowing users to define arbitrary custom layers or functions if possible. Restrict model customization to predefined, safe components within Flux.jl.
  • If custom layers are absolutely necessary, strictly validate and sanitize any user-provided code before incorporating it into the model using Flux.jl. Implement strong input validation and code analysis.
  • Use secure coding practices when implementing custom layers or functions within Flux.jl, avoiding potentially unsafe operations.
  • Consider using a restricted execution environment or sandboxing for custom code execution to limit the potential damage from malicious code within Flux.jl.

Threat: DoS via Model Complexity or Input Size

• Description: An attacker sends specially crafted inputs to the application that trigger computationally expensive operations within the Flux.jl model. This could involve inputs that cause excessive computation time, memory consumption, or GPU utilization during Flux.jl model inference, leading to resource exhaustion and denial of service.
• Impact: Application unavailability, performance degradation for legitimate users, increased infrastructure costs due to resource overload, potential system crashes caused by resource exhaustion during Flux.jl model inference.
• Flux.jl Component Affected: Model Inference, Input Processing (how Flux.jl handles and processes input data for inference).
• Risk Severity: High
• Mitigation Strategies:
  • Implement input size limits and complexity constraints to prevent excessively large or complex inputs from being processed by Flux.jl models.
  • Implement resource monitoring and rate limiting to detect and mitigate DoS attacks targeting Flux.jl inference. Monitor CPU, memory, and GPU usage during Flux.jl operations.
  • Set timeouts for model inference requests to prevent long-running requests from consuming resources indefinitely when using Flux.jl.
  • Use asynchronous processing or queuing mechanisms to handle inference requests and prevent overload on Flux.jl inference services.
  • Consider using resource quotas or containerization to limit resource consumption per request and isolate processes running Flux.jl models.