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Mitigation Strategies Analysis for hydraxman/hibeaver

Mitigation Strategy: Strict Message Schema Validation and Sanitization within HiBeaver Handlers

  • Description:

    1. Define Schemas: Within your hibeaver message handlers, define formal schemas for every expected message type. Use a validation library like pydantic, cerberus, or marshmallow. These schemas define the structure, data types, and constraints of the message payload.
    2. Validate in Handler: Immediately upon receiving a message within the hibeaver handler (using the @subscribe decorator or equivalent), pass the message payload to the validation library.
    3. Reject Invalid Messages: If validation fails, immediately reject the message. hibeaver might not have built-in rejection mechanisms, so you might need to:
      • Log the error.
      • Optionally, send an error response (if the protocol supports it).
      • Crucially, prevent any further processing of the invalid message within the handler. Return early from the handler function.
    4. Sanitize (if necessary): Even after validation, if any part of the message data is used in a potentially dangerous context (file paths, SQL, shell commands, HTML), sanitize it within the handler using appropriate escaping or encoding functions.

  • Threats Mitigated:

    • Remote Code Execution (RCE) (Critical): Prevents injection of malicious code through crafted messages.
    • Injection Attacks (SQLi, XSS, Command Injection) (Critical/High): Sanitization prevents injection of malicious code.
    • Data Corruption (Medium): Ensures only valid data is processed.
    • Denial of Service (DoS) (Medium/High): Rejection of invalid messages prevents resource exhaustion.
    • Bypass of Security Controls (High): Prevents manipulation of application logic via unexpected data.

  • Impact:

    • RCE: Risk significantly reduced.
    • Injection Attacks: Risk significantly reduced.
    • Data Corruption: Risk significantly reduced.
    • DoS: Risk moderately reduced.
    • Bypass of Security Controls: Risk significantly reduced.

  • Currently Implemented:

    • Example: handlers/user_registration.py: The @subscribe("user.registration") handler uses a pydantic model (UserRegistrationMessage) to validate the message payload before any database operations.
    • Example: handlers/comment_processing.py: Sanitization is performed within the handler before database insertion.

  • Missing Implementation:

    • Example: handlers/notification_sender.py: The @subscribe("notification.send") handler does not validate the message schema. It needs a pydantic model and validation logic inside the handler.
    • Example: handlers/file_upload.py: Filename sanitization is missing within the handler.

Mitigation Strategy: Asynchronous Operation Timeouts and Cancellation within HiBeaver Handlers

  • Description:

    1. Identify Long-Running Operations: Within each hibeaver handler, identify any asynchronous operations (network requests, database calls, etc.) that could potentially block or take a long time.
    2. Implement Timeouts: Use asyncio.wait_for to wrap these operations within the handler. Set a reasonable timeout value.
    3. Handle Timeout Exceptions: Catch asyncio.TimeoutError within the handler and handle it gracefully (log, retry, or send an error response). Do not let the exception propagate and crash the handler or the hibeaver event loop.
    4. Implement Cancellation (if applicable): If the handler supports cancellation (e.g., a user cancels a request), use asyncio.Task.cancel() to cancel the relevant task.
    5. Handle Cancellation Exceptions: Within the asynchronous operation, periodically check asyncio.Task.cancelled(). If cancelled, clean up resources and exit gracefully. Handle asyncio.CancelledError appropriately within the handler.

  • Threats Mitigated:

    • Denial of Service (DoS) (High): Prevents handlers from blocking the hibeaver event loop due to long-running operations.
    • Resource Exhaustion (Medium): Prevents excessive resource consumption.
    • Application Unresponsiveness (Medium): Keeps the application responsive.

  • Impact:

    • DoS: Risk significantly reduced.
    • Resource Exhaustion: Risk significantly reduced.
    • Application Unresponsiveness: Risk significantly reduced.

  • Currently Implemented:

    • Example: handlers/external_api_call.py: Network requests within the handler use asyncio.wait_for with a timeout.
    • Example: handlers/data_retrieval.py: Database queries within the handler have timeouts.

  • Missing Implementation:

    • Example: handlers/complex_calculation.py: The computationally intensive calculation within the handler lacks timeouts and cancellation.
    • Example: A global default timeout is missing for all asynchronous operations within handlers.

Mitigation Strategy: Safe Deserialization within HiBeaver Handlers

  • Description:

    1. Avoid pickle: Never use pickle for deserializing message payloads within hibeaver handlers, especially if the messages come from potentially untrusted sources.
    2. Prefer JSON or Protocol Buffers: Use safer serialization formats like JSON or Protocol Buffers for message payloads.
    3. Schema Validation (JSON): If using JSON, always validate the deserialized JSON data against a predefined schema (using pydantic, etc.) within the handler and before using the data.
    4. Safe Deserialization Libraries (if needed): If a more complex format is absolutely required, use a library specifically designed for secure deserialization, and perform this deserialization within the handler.

  • Threats Mitigated:

    • Remote Code Execution (RCE) (Critical): Prevents code injection via malicious serialized data.
    • Injection Attacks (High): Schema validation helps prevent injection attacks.
    • Data Corruption (Medium): Schema validation ensures data validity.

  • Impact:

    • RCE: Risk significantly reduced (near elimination by avoiding pickle).
    • Injection Attacks: Risk significantly reduced (with schema validation).
    • Data Corruption: Risk significantly reduced.

  • Currently Implemented:

    • Example: All handlers use JSON for message payloads.
    • Example: Deserialized JSON is validated against pydantic schemas within most handlers.

  • Missing Implementation:

    • Example: handlers/legacy_data_import.py: This handler still uses pickle. It needs immediate refactoring to use JSON and schema validation within the handler.
    • Example: Some handlers lack rigorous schema validation after JSON deserialization within the handler function itself.