avenir-common

v0.1.5 suspicious
4.0
Medium Risk

Shared Avenir utility modules used by Spectrum Engine

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to network calls that may be used for legitimate purposes but also raise concerns about potential data exfiltration. Additionally, the lack of detailed maintainer information and low-effort metadata suggest caution is warranted.

  • moderate network risk
  • lack of maintainer information
Per-check LLM notes
  • Network: The presence of network calls to an external API could indicate legitimate functionality like fetching updates or sending analytics, but it also raises concerns about potential data exfiltration.
  • Shell: No shell execution patterns detected, suggesting the package does not directly execute system commands which reduces immediate risk associated with command and control activities.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows some low-effort indicators and lacks maintainer information, raising concerns but not definitive evidence of malice.

📦 Package Quality Overall: Low (2.8/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Brief PyPI description (758 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

  • 5 type-annotated function signatures (partial)
○ 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 1.5

Found 1 network call pattern(s)

  • erskeys") response = requests.post( api_url, headers={"Content-Type": "
✓ 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

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 avenir-common 
Create a Python-based utility application named 'AvenirTool' that leverages the functionalities provided by the 'avenir-common' package to manage and manipulate data streams efficiently. This tool should serve as a versatile interface for users to interact with data streams, offering both basic and advanced operations. Here’s a detailed breakdown of what your application should achieve:

1. **Data Stream Management**: Implement functionalities to start, stop, pause, and resume data streams. Use the 'avenir-common' package to handle these operations seamlessly.
2. **Stream Filtering**: Allow users to filter data streams based on specific criteria such as timestamp, source IP, or any other metadata available in the stream packets. Utilize the filtering capabilities provided by 'avenir-common' to process these requests efficiently.
3. **Data Transformation**: Provide options to transform incoming data streams. For example, converting raw data into structured formats like JSON or CSV. Employ the transformation utilities from 'avenir-common' to support these conversions.
4. **Real-time Monitoring**: Integrate real-time monitoring features to display live statistics about the data streams, such as throughput, error rates, and latency. Leverage the monitoring tools within 'avenir-common' to gather and present this information.
5. **Logging and Reporting**: Implement logging mechanisms to record all activities performed on the data streams, including errors and warnings. Additionally, generate periodic reports summarizing the performance of the data streams. Use the logging and reporting features offered by 'avenir-common' to ensure comprehensive documentation.
6. **User Interface**: Develop a user-friendly command-line interface (CLI) that allows users to easily interact with the application. Ensure that the CLI provides clear prompts and feedback.
7. **Configuration Management**: Allow users to configure various settings for the application, such as stream filters, transformation rules, and logging levels, through a configuration file. The 'avenir-common' package should assist in managing these configurations.
8. **Security Enhancements**: Incorporate basic security measures, such as input validation and secure handling of sensitive data, to protect the application from common vulnerabilities.

Your task is to design and implement this application using best practices in Python development. Make sure to thoroughly document your code and include comments explaining how you utilize the 'avenir-common' package at each stage of the application.

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