antares-devkit

v0.8.1 suspicious
5.0
Medium Risk

Provides tools and utility methods to write and debug ANTARES filters with Python.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows a high level of obfuscation, which raises concerns about hidden functionality and intent. However, it lacks other common malicious indicators like network calls, shell executions, and credential risks.

  • High obfuscation risk
  • Lack of author metadata
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 direct system command execution.
  • Obfuscation: The obfuscated code suggests potential attempts to hide the actual functionality of the code, which is suspicious.
  • Credentials: No clear signs of credential harvesting are present in the provided snippets.
  • Metadata: The package has some red flags such as missing author information and lack of secure links, but no clear signs of typosquatting or active malice.

📦 Package Quality Overall: Medium (5.2/10)

✦ High Test Suite 9.0

Test suite present — 7 test file(s) found

  • Test runner config found: conftest.py
  • 7 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://nsf-noirlab.gitlab.io/csdc/antares/devkit/
  • Detailed PyPI description (3407 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

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

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 10.0

Found 5 obfuscation pattern(s)

  • " import torch model.eval() with torch.no_grad(): x = input_features.to(
  • ) self.clf = pickle.loads(buffer.read()) # self.clf = pickle.loads(self.files[
  • .read()) # self.clf = pickle.loads(self.files["cls=binary_n_estimators=100_max_depth=35_rs=11_m
  • t"] self.full_model = pickle.loads(full_model_fn) early_model_fn = self.files["superpho
  • "] self.early_model = pickle.loads(early_model_fn) # subset of features for early-phas
✓ 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: noirlab.edu>

⚠ Suspicious Page Links score 4.0

Found 2 suspicious link(s) on the package page

  • Non-HTTPS external link: http://antares.noirlab.edu
  • Non-HTTPS external link: http://noirlab.edu
✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ 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 antares-devkit 
Your task is to develop a Python-based utility named 'AntaresFilterDebugger' that leverages the 'antares-devkit' package to streamline the development and debugging process of ANTARES filters. This tool aims to assist developers in efficiently writing, testing, and refining their filter scripts by providing an interactive environment with real-time feedback.

The application should include the following key features:
1. **Filter Script Editor**: A simple text editor within the application where users can write their ANTARES filter scripts.
2. **Syntax Highlighting and Validation**: Automatically highlight syntax errors and validate the script as it is being written, providing immediate feedback to the user.
3. **Interactive Debugging Mode**: Allow users to run their scripts in an interactive mode where they can set breakpoints, inspect variables, and step through the code execution.
4. **Real-Time Logging**: Display logs of the filter execution in real-time, helping users understand the flow of data and identify issues quickly.
5. **Customizable Templates**: Provide a library of pre-defined templates for common filter scenarios to help users get started faster.
6. **Export Functionality**: Once a filter script is finalized, allow users to export it into a format compatible with ANTARES systems.

To utilize the 'antares-devkit' package effectively, consider the following steps:
- Use the package's utilities for syntax validation and error checking during the editing phase.
- Leverage its debugging capabilities to implement the interactive debugging feature.
- Utilize logging functions provided by the package to capture and display real-time execution details.
- Explore the template creation functionality offered by the package to facilitate the export of customized filter scripts.

This project will not only enhance the productivity of ANTARES developers but also serve as a practical example of integrating specialized packages into custom applications.

💬 Discussion Feed

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