axiom-axle

v1.3.0 suspicious
4.0
Medium Risk

Python client for AXLE (Axiom Lean Engine) API

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of potential misuse due to sparse metadata and a possibly new or inactive account, although direct malicious activities such as shell execution, obfuscation, or credential harvesting have not been detected.

  • Sparse and suspicious metadata
  • Possibly new or inactive author account
Per-check LLM notes
  • Network: The network calls appear to be standard HTTP/HTTPS requests for status updates, which could be legitimate for monitoring or reporting purposes.
  • Shell: No shell execution patterns detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, suggesting the package is not attempting to steal secrets.
  • Metadata: The author's details are sparse and the account seems new or inactive, which raises some suspicion but not enough to conclude malice.

📦 Package Quality Overall: Medium (5.2/10)

◈ 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://axle.axiommath.ai/v1/docs/
  • Detailed PyPI description (4792 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 78 type-annotated function signatures detected in source
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 10 commits in AxiomMath/axiom-lean-engine
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

  • try: response = requests.get( f"{self.url}/v1/status", timeout=timeout_se
  • lf._http2: return httpx.AsyncClient( http2=True, limits=httpx.Li
  • load ) return aiohttp.ClientSession( trust_env=True, connector=connector
✓ Code Obfuscation

No obfuscation patterns detected

✓ 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: axiommath.ai>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository AxiomMath/axiom-lean-engine 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 axiom-axle 
Create a mini-application that allows users to analyze and visualize their log data using the Axiom Lean Engine (AXLE) via the Python package 'axiom-axle'. This tool will enable users to upload log files, query them using AXLE's powerful search capabilities, and generate visualizations of the queried data.

Step 1: Set up the Project
- Initialize a new Python project.
- Install the 'axiom-axle' package.
- Configure your AXLE instance credentials securely.

Step 2: Implement Data Upload Functionality
- Design a user interface or command-line tool for uploading log files.
- Use the 'axiom-axle' package to send these logs to your AXLE instance.

Step 3: Develop Querying Capabilities
- Create a feature where users can write queries to filter their logs.
- Utilize the 'axiom-axle' package to execute these queries against the AXLE API.
- Display the results of these queries back to the user.

Step 4: Integrate Visualization Tools
- Choose a visualization library like matplotlib or seaborn.
- Implement functionality to generate visual representations of the queried data.
- Allow users to customize basic aspects of these visualizations.

Step 5: Enhance User Experience
- Add error handling for better user experience.
- Provide documentation and examples on how to use the application effectively.
- Ensure the application is user-friendly and accessible.

Suggested Features:
- Support for different types of log formats.
- Real-time querying and updating of results.
- Export options for visualizations and query results.
- Integration with other data analysis tools or platforms.

How to Utilize 'axiom-axle':
- For sending logs to AXLE, use the 'axiom-axle' package's logging functions.
- For querying logs, utilize the package's query execution methods.
- Leverage any additional features provided by 'axiom-axle' to enhance the application's functionality.

💬 Discussion Feed

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