Challenge Overview

Welcome to SonicShield AI, a free online showcase where musicians can feature their new compositions. To ensure a pristine listening experience, the platform uses a sophisticated AI, Arora, to analyze and approve all submissions. Arora is engineered to maintain quality by accepting only musical tracks and rejecting files that contain speech. The target machine runs Ubuntu and is reachable at IP address **10.10.1.122**. Using the supplied music.wav (**http: //10.10.1.122/music.wav**), create an adversarial audio file that remains perceptually a musical track (so Arora still classifies it as music) but causes Arora's transcription pipeline to output a hidden textual payload. Upload the adversarial file via the web UI and retrieve the flag displayed when the model is successfully fooled.

What You Will Learn

  • Identifying exposed services and understanding how the SonicShield AI platform processes audio inputs.
  • Analyzing how AI models classify audio (music vs speech) and generate transcriptions.
  • Crafting adversarial inputs using gradient-based perturbations with tools like TensorFlow.
  • Embedding hidden textual data in audio while bypassing classification filters like Arora AI.
  • Triggering unintended behavior in the transcription system to reveal the hidden message and retrieve the flag.

Program Curriculum

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