🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of potential code injection due to the use of eval and exec, while other risks are minimal. The missing maintainer metadata adds to the suspicion.

High obfuscation risk due to eval/exec usage
Missing maintainer metadata

Per-check LLM notes

Network: No network calls detected, which is normal for a library focused on dependency injection and asynchronous programming.
Shell: No shell execution patterns detected, aligning with the expected behavior of a utility package.
Obfuscation: The use of eval and exec with compilation suggests potential for code injection or obfuscation, raising concerns about security.
Credentials: No patterns indicative of credential harvesting were found.
Metadata: The maintainer's author name is missing and they appear to be new or inactive, which raises some concerns but not enough to definitively label it as malicious.

📦 Package Quality Overall: Medium (5.6/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

Test runner config found: pyproject.toml

◈ Medium Documentation 7.0

Some documentation present

Documentation URL: "Documentation" -> https://notypecheck.github.io/aioinject/
19 documentation file(s) (e.g. example.py)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

◈ Medium Type Annotations 5.0

Partial type annotation coverage

222 type-annotated function signatures detected in source

✦ High Multiple Contributors 8.0

Active multi-contributor project

3 unique contributor(s) across 100 commits in notypecheck/aioinject
Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

try: return eval( # noqa: S307 ret_annotation,
pace: dict[str, Any] = {} exec(compiled, namespace, local_namespace) # noqa: S102 return loca
name}", ) compiled = compile(module_src, source_filename, "exec") local_namespace: dict[str, Any] = {} exec(compile

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ 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 notypecheck/aioinject 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 aioinject

Create a fully functional asynchronous web scraping tool using Python that leverages the 'aioinject' library for dependency management. This tool will be designed to scrape multiple websites concurrently, collecting information such as titles, meta descriptions, and keywords from each page. Additionally, it should include functionality to store scraped data into a SQLite database and provide a simple CLI interface for users to specify which URLs they want to scrape.

Steps to follow:
1. Set up the project structure, including directories for services, repositories, and interfaces.
2. Define the main application class that initializes the aioinject container and starts the scraping process.
3. Implement a service responsible for making HTTP requests asynchronously using aiohttp.
4. Create a repository class that interacts with the SQLite database to save scraped data.
5. Develop an interface that specifies methods for scraping operations, ensuring loose coupling between components.
6. Use aioinject to manage dependencies, allowing easy swapping of implementations for different environments or testing scenarios.
7. Integrate argparse to handle command-line arguments for specifying URLs and other options.
8. Ensure proper error handling and logging mechanisms are in place.
9. Write unit tests to verify the correctness of your implementation.

Suggested Features:
- Support for multiple concurrent connections to improve performance.
- Ability to configure timeouts and retry policies for HTTP requests.
- Flexible configuration options for database interactions.
- Logging of scraping activities for monitoring purposes.
- Command-line flags for controlling verbosity and output format.