auteur

v1.0.2 suspicious
6.0
Medium Risk

Client library for fetching and parsing IMDb title data (with AWS WAF challenge handling).

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows high metadata risk due to suspicious git repository activity and a new maintainer history, which raises concerns about potential malicious intent despite having low risks in network, shell, and credential aspects.

  • High metadata risk
  • Suspicious git repository activity
  • New maintainer history
Per-check LLM notes
  • Network: Network calls are expected but should be scrutinized for legitimacy and potential misuse.
  • Shell: No shell execution patterns detected.
  • Obfuscation: No obfuscation patterns detected, suggesting low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: High risk due to suspicious git repository activity and new maintainer history.

📦 Package Quality Overall: Medium (5.0/10)

✦ High Test Suite 9.0

Test suite present — 12 test file(s) found

  • Test runner config found: pyproject.toml
  • 12 test file(s) detected (e.g. person_fixtures.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (2382 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 117 type-annotated function signatures detected in source
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 1 commits in spiritualized/auteur
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

  • try: req = urllib.request.Request(url, headers={"User-Agent": self._c.user_agent()})
  • er_agent()}) with urllib.request.urlopen(req, timeout=30) as r: data = r.read
  • int = 10) -> str: r = requests.get( url, headers={**self._base_headers, "User-Agent
  • ) r = requests.post( f"{url}/mp_verify", files={
✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 5.0

Git history flags: Very few commits: 1 total

  • Very few commits: 1 total
  • Single contributor with only 1 commit(s) — possibly throwaway account
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "spiritualized" 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 auteur 
Create a movie recommendation system using the 'auteur' Python package. This mini-app will allow users to input their favorite movies or TV shows, and based on these inputs, the app will recommend similar titles from IMDb. Here’s a detailed breakdown of the steps and features:

1. **Setup**: Install necessary packages including 'auteur', and any additional libraries like pandas for data manipulation and Flask for web integration.
2. **User Input Interface**: Develop a simple user interface where users can enter titles of movies or TV shows they enjoy. This could be a command-line interface or a basic web form.
3. **Data Retrieval**: Use the 'auteur' package to fetch detailed information about the entered titles, including genres, directors, actors, and plot summaries.
4. **Similarity Calculation**: Implement a similarity algorithm (e.g., cosine similarity based on genre, director, actor tags) to find movies with similar characteristics.
5. **Recommendation Engine**: Based on the calculated similarities, suggest top 5-10 recommendations to the user.
6. **Display Recommendations**: Show the recommended titles along with brief descriptions fetched via 'auteur'.
7. **Optional Features**: Consider adding options for filtering recommendations by year, rating, or genre; and storing user preferences locally or remotely.
8. **Testing**: Ensure the application works as expected by testing with various inputs and edge cases.
9. **Deployment**: Optionally deploy the application as a web service so it can be accessed online.

This project will showcase your ability to integrate external APIs, handle data retrieval and processing, and implement basic machine learning techniques for content-based recommendation systems.

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