admixture-cache

v1.4.2 suspicious
4.0
Medium Risk

Precomputed-P supervised-ADMIXTURE projection cache: build slow once, project fast per target.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risk due to potential shell execution risks and low activity indicators, which may suggest it is either new or less maintained. This combination warrants closer scrutiny.

  • Moderate shell risk indicating potential command injection vulnerabilities
  • Low metadata activity suggesting the package may be new or less trusted
Per-check LLM notes
  • Network: Network calls are likely for fetching updates or dependencies, but should be reviewed to ensure they align with the package's intended functionality.
  • Shell: Shell executions might be used for running version checks or other utilities, but could pose risks if commands are not properly sanitized or controlled.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, suggesting safe handling of sensitive information.
  • Metadata: The package shows signs of potential low activity or newness, with an anonymous author and minimal engagement in the git repository.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

  • try: req = urllib.request.Request( next_url, headers={"Accept": "appli
  • ) with urllib.request.urlopen(req, timeout=timeout) as resp: paylo
  • """ try: with urllib.request.urlopen(url, timeout=timeout) as resp: content =
  • try: req = urllib.request.Request( release.tarball_url,
  • ) with urllib.request.urlopen(req, timeout=timeout) as resp: # Som
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • try: proc = subprocess.Popen( cmd, cwd=cwd, stdout=log_file, stderr=s
  • le.""" try: out = subprocess.run( [binary, "--version"], capture_output=True, tex
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: mailbox.org>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ 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 admixture-cache 
Create a genetic data analysis tool called 'GenoProjector' using the Python package 'admixture-cache'. This tool will enable researchers to efficiently analyze genetic admixture data from various populations by leveraging precomputed projections. Here’s how you can build it:

1. **Setup Environment**: Begin by setting up your Python environment. Install necessary packages including 'admixture-cache', 'pandas', and 'numpy'. Ensure that 'admixture-cache' is correctly installed and accessible.

2. **Data Preparation**: Design a function to load and preprocess genetic data files (e.g., CSV or TXT). This includes cleaning the data, handling missing values, and ensuring it is compatible with 'admixture-cache'.

3. **Admixture Projection**: Implement a feature where users can input their genetic dataset and select a precomputed ADMIXTURE model from 'admixture-cache'. Use 'admixture-cache' to project new datasets onto these models quickly without having to recompute them.

4. **Visualization**: Add functionality to visualize the projected admixture results using libraries like 'matplotlib' or 'seaborn'. Allow users to customize plots such as color schemes, labels, and axes.

5. **Interactive Interface**: Develop an interactive command-line interface (CLI) where users can easily navigate through different functionalities of GenoProjector. Consider adding options for saving outputs, viewing help documentation, and more.

6. **Documentation and Testing**: Write comprehensive documentation explaining how to use GenoProjector, including examples and best practices. Also, implement unit tests to ensure all parts of the application work as expected.

**Suggested Features**:
- Support for multiple precomputed ADMIXTURE models.
- Option to save and load custom ADMIXTURE models.
- Integration with cloud storage services for larger datasets.
- Advanced plotting options such as heatmaps and dendrograms.
- Detailed logging to track operations and errors.