🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of potential misuse, primarily due to shell execution and possible obfuscation techniques. However, there are no clear indicators of malicious intent or supply-chain attacks.

Shell execution detected
Possible misuse of .eval() method

Per-check LLM notes

Network: No network calls detected, which is typical for many packages.
Shell: Shell execution detected may indicate the package is intended to run external commands, but could also be a sign of potential security risks if not properly sanitized.
Obfuscation: The detected patterns appear to be misuses of the .eval() method which is typically used for evaluation mode in neural networks, but their context suggests they might be mistakenly placed or used for obfuscation.
Credentials: No suspicious patterns indicative of credential harvesting were found.
Metadata: The maintainer's author name is missing or very short and has only one package, which could indicate a less experienced or potentially suspicious user.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

able() model.backbone.eval() # Switch back to flash/efficient SDP for the first-or
lf.device) self.model.eval() # ---------------------------------------------------
raw = raw.to(device).eval() print(f" input_shape={raw.input_shape} n_classes
SonicsWrapper(raw).to(device).eval() return raw, wrapped, args.sample_rate if args
model = model.to(device).eval() return model, model, AST_SAMPLE_RATE if args.
model = model.to(device).eval() return model, model, VGGISH_SAMPLE_RATE raise

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

oin(cmd)}") # noqa: T201 subprocess.run(cmd, check=True) def main() -> None: pyproject = tomll

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository cncPomper/Audio-XAI appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with Audio-XAI

Create a mini-application named 'AudioInsight' using the 'Audio-XAI' package which focuses on enhancing user understanding of audio files through explainable AI techniques. The application should allow users to upload any audio file, and then provide insights into the audio content in a comprehensible manner. Here are the key steps and features for your application:

1. **User Interface**: Develop a simple yet effective web-based interface where users can upload their audio files.
2. **File Processing**: Once an audio file is uploaded, the application should process it using the 'Audio-XAI' package to extract meaningful insights such as speech segments, music genres, ambient sounds, etc.
3. **Insight Visualization**: Display these insights in a visually appealing way, perhaps through charts or graphs. For example, a timeline showing different sound types over time.
4. **Explainability**: Use 'Audio-XAI' to provide explanations behind each insight, helping users understand why certain parts of the audio were classified as they were. This could include visualizations of sound wave patterns, spectrograms, and other relevant data representations.
5. **Export Options**: Allow users to download the processed insights in a format like PDF or CSV for further analysis or record-keeping.
6. **Feedback Loop**: Implement a feature where users can give feedback on the accuracy of the insights provided by the application, which could help improve future analyses.

The goal is to make complex audio analysis accessible and understandable to non-experts, thereby democratizing the use of AI in audio processing.