aidsc

v0.1.6 suspicious
6.0
Medium Risk

Package for Inferencing AI at DSC

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has notable risks associated with credential harvesting and suspicious metadata, which raises concerns about its legitimacy and potential for malicious activity.

  • High credential risk due to usage of 'getpass' and request for user input.
  • Suspicious metadata including a non-HTTPS link and missing repository information.
Per-check LLM notes
  • Network: The use of HTTP requests is common for packages that need to fetch external resources or communicate with servers.
  • Shell: Executing shell commands can be risky as it may lead to unintended system changes or vulnerabilities; however, some legitimate uses might include version control operations.
  • Obfuscation: No obfuscation patterns detected in the code snippet.
  • Credentials: The code imports 'getpass' and requests user input, potentially for harvesting credentials.
  • Metadata: Suspicious non-HTTPS link and missing repository indicate potential risk.

📦 Package Quality Overall: Low (4.2/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. test_runtime_payload.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (5643 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

  • 18 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • try: with httpx.Client(timeout=self._timeout) as client: r = client
  • _REQUESTS_PATH}" with httpx.Client(timeout=self._timeout) as client: r = client.pos
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

  • ── def _clear() -> None: os.system("cls" if os.name == "nt" else "clear") def _banner(title:
  • self._tunnel_proc = subprocess.Popen( cmd, stdout=subprocess.DEVN
  • .cwd() try: out = subprocess.run( ["git", "rev-parse", "--show-toplevel"],
⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • import getpass return getpass.getpass(f" ➤ {label}: ").strip() return input(f" ➤ {label}:
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://127.0.0.1:32553
⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Brendon DGR" 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 aidsc 
Your task is to develop a user-friendly mini-application using the 'aidsc' package, which is designed for inferencing AI models at Data Science Competitions (DSC). This application will serve as a bridge between users who have basic knowledge of AI and those who want to quickly apply pre-trained models to their datasets without diving into complex setup procedures. The app should allow users to upload a dataset, select a pre-configured AI model from a list provided by the 'aidsc' package, and then perform inference on the uploaded data. Here’s a detailed breakdown of what your application should include:

1. **User Interface**: Design a clean and intuitive UI where users can easily navigate through the application. It should include sections for uploading files, selecting models, viewing results, and providing feedback.

2. **Dataset Upload**: Implement functionality that allows users to upload CSV or Excel files containing their dataset. Ensure that the application checks for file type validity and handles large datasets efficiently.

3. **Model Selection**: Integrate the 'aidsc' package to provide a dropdown menu with various pre-configured AI models suitable for different types of datasets (e.g., classification, regression, clustering). Each model should come with a brief description of its purpose and capabilities.

4. **Inference Process**: Once a model is selected and the dataset is uploaded, the application should use the 'aidsc' package to perform inference on the data. Display progress indicators and ensure that the process is executed smoothly.

5. **Result Presentation**: After inference, present the results in a clear and understandable format. Include visualizations such as charts and graphs if applicable. Allow users to download the results in a preferred format (CSV, Excel, PDF).

6. **Feedback Mechanism**: Incorporate a feature where users can rate the accuracy and usefulness of the inferred results. Collect this feedback to improve future versions of the application.

7. **Documentation**: Provide comprehensive documentation that explains how to use the application effectively, including examples and FAQs.

To utilize the 'aidsc' package, focus on leveraging its capabilities for easy model deployment and inference. Ensure that you handle any errors gracefully and provide meaningful error messages to guide users. Your goal is to make AI accessible and straightforward for everyone.

💬 Discussion Feed

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