anaconda-ai

v0.7.0 suspicious
5.0
Medium Risk

Download and launch curated models from Anaconda

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has moderate risks associated with network calls and credential handling, which could potentially be exploited for unauthorized activities. Additionally, the low maintainer activity and lack of essential metadata raise concerns about its reliability and security.

  • network risk due to external URL calls
  • credential risk due to AWS credentials handling
  • low maintainer activity and missing metadata
Per-check LLM notes
  • Network: Network calls to external URLs and tokenization services might be legitimate depending on the package's intended functionality, but require further investigation to ensure they are not being used for unauthorized data transfer.
  • Shell: No shell execution patterns detected, which is normal and indicates no immediate risk from shell command execution.
  • Obfuscation: No obfuscation patterns detected in the code.
  • Credentials: The presence of instructions to configure AWS credentials might indicate the package requires AWS services, but it's risky as it could guide users into exposing their credentials directly within the package context.
  • Metadata: The package shows low maintainer activity and lacks essential metadata, indicating potential low-quality or abandoned status.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 5 test file(s) found

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

Some documentation present

  • Detailed PyPI description (25067 chars)
○ 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

  • 219 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 score 4.5

Found 3 network call pattern(s)

  • load_url"] response = requests.get(signed_url, stream=True) response.raise_for_status(
  • url"] response = requests.get(signed_url, stream=True) response.raise_for_stat
  • ), "/tokenize") res = httpx.post(url, json={"content": message}) if not res.is_succes
✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • " - Configure ~/.aws/credentials\n" " - Run: aws configure" )
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with anaconda-ai 
Develop a Python-based mini-application named 'ModelMixer' that leverages the 'anaconda-ai' package to streamline the process of downloading and launching machine learning models from Anaconda's repository. This tool should enable users to easily select from a variety of pre-curated models, download them directly into their local environment, and then run inference on provided datasets or custom inputs.

Key Features:
1. User-friendly interface to browse available models categorized by task (e.g., image classification, natural language processing).
2. Integration with 'anaconda-ai' to handle the download and setup of selected models.
3. Option to configure model parameters before running inference.
4. Support for multiple input formats (e.g., images, text files) depending on the model's requirements.
5. Display of inference results in a readable format.
6. Logging of each operation for auditing and troubleshooting purposes.

Steps to Build:
1. Install the 'anaconda-ai' package using pip.
2. Design a simple command-line interface (CLI) or graphical user interface (GUI) for user interaction.
3. Implement functions to fetch and display a list of available models from the 'anaconda-ai' repository.
4. Create functionality to allow users to choose a model, set up its environment, and download it locally.
5. Develop an inference engine within 'ModelMixer' that accepts input data and runs predictions using the downloaded model.
6. Ensure outputs are presented clearly and can be saved for further analysis if needed.
7. Incorporate logging mechanisms to track all operations performed through 'ModelMixer'.

The goal of this project is to provide developers and enthusiasts with a quick and easy way to experiment with different machine learning models without needing to manually set up environments or worry about dependencies.

💬 Discussion Feed

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