AI Analysis
The package exhibits moderate risks due to incomplete obfuscation and questionable metadata, including a lack of maintainer history and minimal repository activity. While there are no direct indicators of malicious intent or actions, the overall profile raises concerns about potential supply-chain vulnerabilities.
- Incomplete obfuscation
- Lack of maintainer history
- Minimal repository activity
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- Shell: No shell execution patterns detected, indicating no immediate signs of malicious activity.
- Obfuscation: The obfuscation appears to be incomplete and possibly accidental, not indicative of malicious intent.
- Credentials: No patterns indicating credential harvesting were detected.
- Metadata: The package shows several red flags including a lack of maintainer history, minimal repository activity, and an incomplete author profile.
Package Quality Overall: Low (4.8/10)
Partial test coverage signals detected
Test runner config found: pyproject.toml
Some documentation present
Detailed PyPI description (9338 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
Partial type annotation coverage
35 type-annotated function signatures detected in source
Limited contributor diversity
2 unique contributor(s) across 16 commits in andreaaresu/asr-attackTwo distinct contributors found
Heuristic Checks
No suspicious network call patterns found
Found 1 obfuscation pattern(s)
model = model.to(self.device).eval() self._processor = AutoProcessor.from_pretrained(se
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
Email domain looks legitimate: gmail.com>
All external links appear legitimate
Git history flags: Repository has zero stars and zero forks
Repository has zero stars and zero forks
3 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a Python-based application that demonstrates the vulnerabilities of Automatic Speech Recognition (ASR) models against adversarial attacks using the 'asr-attack' package. Your application should include the following functionalities: 1. **Model Selection**: Allow users to select from a variety of Hugging Face ASR models available through the 'transformers' library. 2. **Audio Input**: Provide a feature for users to upload their own audio files or generate synthetic speech for testing purposes. 3. **Attack Generation**: Implement different types of adversarial attack methods provided by 'asr-attack', such as Perturbation-based attacks or Noise Injection, to alter the input audio slightly without significantly changing its perceptual quality. 4. **Accuracy Testing**: Compare the ASR model's performance on the original audio vs. the attacked audio, showing the drop in accuracy due to the adversarial manipulation. 5. **Visualization**: Offer visual representations of the attacks' effects on both the audio waveforms and the ASR model's confidence scores. 6. **Report Generation**: Automatically create a report summarizing the experiment results, including the type of attack used, the level of perturbation introduced, and the impact on recognition accuracy. Your task is to design a user-friendly interface (command-line or graphical) that guides users through these steps, making it easy for them to understand the implications of adversarial attacks on ASR systems. Additionally, ensure your application includes comprehensive documentation and clear instructions for setup and usage.
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