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attack-surface.md

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Attack Surface Analysis for coqui-ai/tts

Attack Surface: Model Poisoning/Backdooring

  • Description: An attacker replaces a legitimate TTS model with a malicious one, or modifies an existing model to introduce harmful behavior.
  • TTS Contribution: The core functionality of Coqui TTS relies on pre-trained or fine-tuned models. These models are the direct target.
  • Example: An attacker provides a poisoned model that subtly changes the pronunciation of certain words to spread misinformation, or inserts barely audible commands.
  • Impact: Loss of data integrity, manipulation of generated speech, potential execution of arbitrary commands (if output is fed to a system that interprets commands), reputational damage.
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Trusted Sources: Download models only from the official Coqui repository or a meticulously vetted internal source.
    • Checksum Verification: Always verify the SHA-256 checksum of downloaded models against the official checksum. Reject any mismatch.
    • Signature Verification: If Coqui provides signed models, verify the digital signature before loading.
    • Regular Audits: Periodically re-verify the integrity of deployed models.
    • Sandboxing (Advanced): Run the model inference in a highly restricted, isolated environment.

Attack Surface: Adversarial Examples (Model Evasion)

  • Description: An attacker crafts specific input text designed to cause the TTS model to malfunction, produce unexpected output, or consume excessive resources.
  • TTS Contribution: The TTS engine's core function is to process text input and generate audio. This input processing is directly vulnerable.
  • Example: An attacker inputs a string of carefully chosen Unicode characters that cause the model to generate extremely high-frequency sounds or enter an infinite loop.
  • Impact: Denial-of-service, unexpected audio output, potential hardware damage (extreme cases), resource exhaustion.
  • Risk Severity: High
  • Mitigation Strategies:
    • Input Length Limits: Enforce strict maximum lengths for input text.
    • Character Filtering: Restrict the allowed character set. Block unusual Unicode characters.
    • Input Normalization: Normalize input text before processing.
    • Rate Limiting: Limit the number of TTS requests per user/IP.
    • Robustness Training (Advanced): Train the model with adversarial examples.

Attack Surface: Denial-of-Service (DoS) via Resource Exhaustion

  • Description: An attacker overwhelms the TTS system with requests, or sends requests designed to consume excessive resources.
  • TTS Contribution: TTS processing, especially with deep learning models, is computationally expensive. This makes the TTS engine itself a direct target.
  • Example: An attacker sends thousands of simultaneous requests or requests with extremely long input texts.
  • Impact: Service unavailability, disruption of service for legitimate users.
  • Risk Severity: High
  • Mitigation Strategies:
    • Rate Limiting (Essential): Implement strict rate limiting per user/IP.
    • Input Length Limits (Essential): Enforce strict limits on input text length.
    • Resource Quotas: Set resource limits (CPU, memory) for the TTS process/container.
    • Timeouts: Implement timeouts for TTS requests.
    • Load Balancing: Distribute requests across multiple servers.
    • Asynchronous Processing: Use asynchronous task queues.

Attack Surface: Dependency Vulnerabilities

  • Description: Vulnerabilities in the libraries that Coqui TTS depends on (e.g., PyTorch, TensorFlow) could be exploited.
  • TTS Contribution: Coqui TTS, relies on a complex web of dependencies. These dependencies are a potential source of vulnerabilities directly impacting TTS.
  • Example: A vulnerability is discovered in a specific version of PyTorch that allows for remote code execution. An attacker exploits this vulnerability to gain control of the server running Coqui TTS.
  • Impact: Remote code execution, system compromise, data breach.
  • Risk Severity: High to Critical (depending on the vulnerability)
  • Mitigation Strategies:
    • Dependency Management: Use a robust dependency management system.
    • Vulnerability Scanning: Regularly scan dependencies for known vulnerabilities.
    • Prompt Updates: Keep dependencies up-to-date.
    • Dependency Pinning: Pin dependency versions (balance with security updates).
    • Vendor Advisories: Monitor security advisories from dependency vendors.