mitigations.md

Mitigation Strategies Analysis for ml-explore/mlx

Mitigation Strategy: Model Origin Validation for MLX Models

• Description:

  1. Establish Trusted MLX Model Sources: Define and document trusted sources for all mlx models used in the application. This explicitly dictates where mlx models are allowed to originate from (e.g., internal repositories, verified research groups).
  2. Implement Digital Signatures for MLX Models: Implement a process to digitally sign mlx model files after training and validation. This uses cryptography to ensure the integrity and authenticity of the mlx models themselves.
  3. MLX Model Signature Verification in Application: Within the application code that loads models into mlx, implement a verification step. Before mlx loads a model file, verify its digital signature against a known public key. This step is crucial before mlx processes the model.
  4. MLX Model Loading Rejection on Verification Failure: If signature verification fails, the application must refuse to load the model into mlx and log a security error. This prevents mlx from using potentially compromised models.
  5. Secure Key Management for MLX Model Signing: Securely manage the cryptographic keys used for signing and verifying mlx models. This is essential to maintain the trust in the model validation process for mlx.

• Threats Mitigated:

  • Malicious MLX Model Injection (High Severity): An attacker replaces a legitimate mlx model with a malicious one, directly impacting mlx's operations and the application's behavior.
  • MLX Model Supply Chain Compromise (Medium Severity): If the source of mlx models is compromised, attackers can inject malicious models that will be loaded and used by mlx, bypassing security if origin is not validated.

• Impact:

  • Malicious MLX Model Injection: Significantly reduces the risk of mlx loading and using unauthorized or malicious models.
  • MLX Model Supply Chain Compromise: Moderately reduces the risk by ensuring that even if a model source is compromised, mlx will reject models without valid signatures.

• Currently Implemented: To be determined. Check if the application currently validates the origin of mlx models before loading them into the mlx runtime. Specifically, examine the code responsible for model loading into mlx.

• Missing Implementation: Likely missing in the mlx model loading process if not explicitly designed. Implementation is needed in the application's model loading module, specifically in the code that interacts with mlx to load models.

Mitigation Strategy: Model Input Validation and Sanitization for MLX Inference

• Description:

  1. Define Input Schema for MLX Models: Clearly define the expected schema and data types for inputs that will be fed into your mlx models for inference. This schema is specific to the input requirements of the models used by mlx.
  2. Implement Input Validation Logic Before MLX Inference: Implement validation logic in your application code before passing input data to mlx for inference. This logic checks if the input conforms to the defined schema before mlx processes it.
  3. Sanitize Input Data Before MLX Inference: Sanitize input data to remove or neutralize potentially malicious or unexpected characters or formats before it is used as input to mlx. This protects mlx from processing potentially harmful inputs.
  4. Handle Invalid Inputs Before MLX Inference: If input data fails validation, handle it gracefully before it reaches mlx. Prevent invalid data from being processed by mlx models.
  5. Regularly Update Validation Rules for MLX Models: As mlx models and application evolve, regularly review and update input validation rules to ensure they remain effective for the specific models used with mlx.

• Threats Mitigated:

  • Adversarial Input Attacks on MLX Models (High Severity): Attackers craft specific inputs designed to manipulate the behavior of mlx models, leading to incorrect or malicious outputs from mlx.
  • Injection Attacks via MLX Model Inputs (Medium Severity): If mlx model inputs are derived from external sources, input validation can prevent injection attacks that could indirectly affect mlx or the application.

• Impact:

  • Adversarial Input Attacks on MLX Models: Significantly reduces the risk of attackers manipulating mlx model behavior through crafted inputs.
  • Injection Attacks via MLX Model Inputs: Moderately reduces the risk of injection attacks that could exploit vulnerabilities through mlx model inputs.

• Currently Implemented: To be determined. Check if input validation is performed before data is passed to mlx models for inference. Look for validation routines in the code paths that precede calls to mlx inference functions.

• Missing Implementation: Potentially missing in data preprocessing stages or API input handling layers before data is used for mlx inference. Validation logic needs to be implemented in the code that prepares data for mlx.

Mitigation Strategy: Resource Limits for MLX Processes

• Description:

  1. Analyze MLX Process Resource Usage: Analyze the typical resource consumption (CPU, memory, GPU) of processes running mlx models under normal load. Understand the resource footprint of mlx itself.
  2. Set Resource Limits for MLX Processes: Configure resource limits specifically for the processes that are executing mlx code. This prevents any single mlx process from monopolizing system resources.
  3. Monitor MLX Process Resource Usage: Implement monitoring to track the resource usage of mlx processes in real-time. Monitor resources consumed by mlx to detect anomalies.
  4. Enforce Limits and Handle MLX Process Exceedances: Ensure resource limits are actively enforced on mlx processes. Define how the application should respond if mlx processes exceed resource limits.
  5. Regularly Review and Adjust MLX Process Limits: Periodically review and adjust resource limits for mlx processes based on changes in model complexity, application load, and infrastructure capacity related to mlx usage.

• Threats Mitigated:

  • Denial of Service (DoS) via MLX Resource Exhaustion (High Severity): Attackers exploit mlx or its models to exhaust system resources, specifically those used by mlx processes, causing DoS.
  • Resource Starvation by MLX Processes (Medium Severity): Malicious or inefficient mlx models/inputs could consume excessive resources, starving other parts of the application or other applications sharing resources with mlx.

• Impact:

  • Denial of Service (DoS) via MLX Resource Exhaustion: Significantly reduces the risk of DoS caused by uncontrolled resource consumption by mlx processes.
  • Resource Starvation by MLX Processes: Moderately reduces the risk of resource starvation caused by individual mlx processes.

• Currently Implemented: To be determined. Check if resource limits are configured for processes specifically running mlx components in the deployment environment.

• Missing Implementation: Potentially missing in the deployment configuration, especially if running mlx processes without containerization or explicit resource management. Resource limits need to be configured at the infrastructure level where mlx is executed.

Mitigation Strategy: Secure Dependency Management for MLX and its Dependencies

• Description:

  1. Maintain MLX Dependency Inventory: Create and maintain a detailed inventory of all dependencies used by mlx and your application, including transitive dependencies. Focus on the libraries that mlx relies upon.
  2. Vulnerability Scanning for MLX Dependencies: Regularly scan the dependency inventory of mlx for known vulnerabilities using security scanning tools. Specifically target the libraries that mlx uses.
  3. Patch Management and Updates for MLX Dependencies: Promptly apply security patches and updates to mlx and its dependencies when vulnerabilities are identified. Follow security advisories related to mlx and its ecosystem.
  4. Dependency Pinning for MLX Dependencies: Use dependency pinning to specify exact versions of mlx and its dependencies in your project's dependency files. This ensures consistent builds and reduces risks from unexpected updates to libraries used by mlx.
  5. Private Dependency Mirror for MLX Dependencies (Optional): Consider a private mirror for packages used by mlx to control and pre-scan dependencies before they are used in your environment.

• Threats Mitigated:

  • Exploitation of Vulnerabilities in MLX Dependencies (High Severity): Vulnerabilities in mlx's dependencies can be exploited to compromise the application or system running mlx.
  • Supply Chain Attacks Targeting MLX Dependencies (Medium Severity): Compromised or malicious dependencies of mlx could be introduced through the dependency supply chain.

• Impact:

  • Exploitation of Vulnerabilities in MLX Dependencies: Significantly reduces the risk of exploitation by proactively managing and patching vulnerabilities in mlx's dependencies.
  • Supply Chain Attacks Targeting MLX Dependencies: Moderately reduces the risk by increasing visibility and control over the dependencies used by mlx.

• Currently Implemented: To be determined. Check dependency management practices for the project, focusing on how mlx and its dependencies are managed.

• Missing Implementation: Potentially missing if dependency management for mlx and its ecosystem is not systematically enforced. Implementation involves setting up scanning, patching, and potentially a private mirror for mlx dependencies.

Mitigation Strategy: ML Specific Logging and Monitoring for MLX Operations

• Description:

  1. Identify MLX-Specific Events for Logging: Determine which events related to mlx operations are security-relevant and should be logged. Focus on events directly related to mlx such as model loading by mlx, inference requests processed by mlx, and errors within mlx.
  2. Implement Detailed Logging for MLX Operations: Implement logging in your application code to capture the identified mlx-specific events. Log events related to mlx's behavior and interactions.
  3. Centralized Log Management for MLX Logs: Centralize logs from all application components, including those related to mlx, into a secure log management system. Ensure logs from mlx operations are included.
  4. Anomaly Detection and Alerting for MLX Events: Configure anomaly detection rules and alerts in your monitoring system to identify unusual patterns in mlx-specific logs. Monitor logs for unexpected behavior from mlx.
  5. Regular Log Review and Analysis of MLX Logs: Regularly review and analyze mlx-specific logs to identify potential security incidents or unexpected behavior related to mlx and its models.

• Threats Mitigated:

  • Delayed Detection of Attacks Targeting MLX (Medium Severity): Without specific logging of mlx operations, it's harder to detect attacks targeting the ML components using mlx.
  • Difficulty in Forensic Analysis of MLX Incidents (Medium Severity): Lack of detailed mlx logs makes forensic analysis challenging after security incidents involving mlx.

• Impact:

  • Delayed Detection of Attacks Targeting MLX: Moderately reduces the risk by providing visibility into mlx-specific events that can indicate attacks.
  • Difficulty in Forensic Analysis of MLX Incidents: Moderately reduces the risk by providing logs for post-incident analysis of security events related to mlx.

• Currently Implemented: To be determined. Check the application's logging infrastructure and see if it captures events specifically related to mlx operations.

• Missing Implementation: Potentially missing if logging is generic and doesn't specifically capture mlx-related events. Implementation involves adding logging for mlx operations and ensuring these logs are part of the centralized logging system.