1. Python with NumPy: pip3 install numpy
2. wave module (standard library)

1. Download challenge.wav from the challenge page to your local machine.
2. Play the audio file. You will hear a simple tone, nothing unusual audibly.
3. Use file challenge.wav to confirm it's a valid WAV file.
4. Run strings challenge.wav | grep -i flag to search for any obvious references to flags in the file.

1. Suspecting steganography, write a Python script to extract the least significant bits from the audio samples.
2. Use the wave and numpy modules to read the audio data.

1. Read the first len(flag) * 8 samples from the audio file.
2. Extract the LSB from each sample and group them into bytes.
3. Convert each byte to its ASCII character.
4. The result is the flag: 3f2175fe-f40c-496c-873e-4b91e1611ff0

import wave
import numpy as np
with wave.open('challenge.wav', 'rb') as f:
    frames = f.readframes(f.getnframes())
    audio = np.frombuffer(frames, dtype=np.int16)
bits = [str(sample & 1) for sample in audio[:36*8]]
flag = ''.join([chr(int(''.join(bits[i*8:(i+1)*8]), 2)) for i in range(36)])
print(flag)

• LSB Steganography in Audio: Hiding data in the least significant bits of audio samples is a common steganography technique used by attackers to exfiltrate data covertly.
• Detection Methods: Automated tools and scripts can detect LSB steganography by analyzing sample value distributions.
• Security Implications: LSB steganography can be used for covert data exfiltration, hiding malicious code, or communicating secretly. It's often used in advanced persistent threats (APTs).
• Countermeasures: Organizations should scan audio files for steganography, use network monitoring to detect unusual data patterns, and implement strict file upload policies.
• Forensic Analysis: When investigating security incidents, always check audio files for hidden data using specialized steganography detection tools.