aind-log-utils

v0.2.8 suspicious
4.0
Medium Risk

Add logging to Code Ocean capsules

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package is deemed suspicious due to its retrieval of potential AWS-related environment variables and the presence of non-secure links, despite being low-risk in other areas.

  • Credential risk from retrieving environment variables
  • Non-secure links and lack of maintainer information
Per-check LLM notes
  • Network: No network calls detected, which is normal for a logging utility package.
  • Shell: No shell execution patterns detected, aligning with expectations for a logging utilities package.
  • Obfuscation: No obfuscation patterns detected.
  • Credentials: The code retrieves environment variables which could be related to AWS services but does not inherently indicate malicious activity; however, it's important to ensure that these credentials are handled securely.
  • Metadata: The package contains non-secure links and lacks maintainer information, raising some concerns but not definitive evidence of malicious intent.

📦 Package Quality Overall: Low (3.4/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 2 test file(s) detected (e.g. __init__.py)
◈ Medium Documentation 7.0

Some documentation present

  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (15396 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
○ 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

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 10.0

Found 6 credential access pattern(s)

  • record.AWS_BATCH_JOB_ID = os.getenv("AWS_BATCH_JOB_ID", "undefined") record.AWS_BATCH_CE_NAME
  • record.AWS_BATCH_CE_NAME = os.getenv("AWS_BATCH_CE_NAME", "undefined") record.AWS_BATCH_JQ_NAM
  • record.AWS_BATCH_JQ_NAME = os.getenv("AWS_BATCH_JQ_NAME", "undefined") record.AWS_METADATA_SER
  • TADATA_SERVICE_NUM_ATTEMPTS = os.getenv( "AWS_METADATA_SERVICE_NUM_ATTEMPTS", "undefined" )
  • ecord.AWS_BATCH_JOB_ATTEMPT = os.getenv( "AWS_BATCH_JOB_ATTEMPT", "undefined" ) record.AWS
  • record.AWS_MAX_ATTEMPTS = os.getenv("AWS_MAX_ATTEMPTS", "undefined") return record retu
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

⚠ Suspicious Page Links score 6.0

Found 3 suspicious link(s) on the package page

  • Non-HTTPS external link: http://eng-logtools:8080/
  • Non-HTTPS external link: http://eng-logtools:3100/loki/api/v1/push
  • Non-HTTPS external link: http://eng-logtools.corp.alleninstitute.org:9000
✓ 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 aind-log-utils 
Your task is to develop a mini-application that enhances the logging capabilities of a hypothetical scientific research project running on Code Ocean. This application will utilize the 'aind-log-utils' Python package to streamline and standardize logging across different components of the project. The goal is to create a tool that not only logs information but also provides insights into the project's progress, potential issues, and performance metrics.

### Step-by-Step Guide:
1. **Setup Environment**: Begin by setting up a Python environment with 'aind-log-utils' installed. Ensure you have all necessary dependencies for the package.
2. **Project Structure**: Design a modular project structure that includes separate modules for data processing, analysis, and visualization. Each module should use 'aind-log-utils' to log relevant information.
3. **Core Features**:
   - **Data Processing Logging**: Implement logging for data preprocessing steps such as cleaning, normalization, and transformation. Use 'aind-log-utils' to capture the state of the data before and after each process.
   - **Analysis Logging**: Log the outcomes of various analytical procedures. This could include statistical tests, model training, or other computational tasks. Ensure logs capture key parameters and results.
   - **Visualization Logging**: Integrate logging for any visualizations produced during the project. Logs should describe the type of visualization, input data, and any specific settings used.
4. **Enhanced Logging Features**:
   - **Error Handling**: Implement robust error handling mechanisms that log exceptions and errors encountered during execution. Use 'aind-log-utils' to categorize these errors and provide context.
   - **Performance Metrics**: Include logging for performance metrics such as execution time, resource usage, and efficiency of algorithms. This will help in optimizing future runs.
5. **User Interface**: Develop a simple user interface or command-line tool that allows users to view logs in real-time or access past logs. The interface should filter and display logs based on severity levels (info, warning, error).
6. **Documentation**: Write comprehensive documentation that explains how to set up the project, use 'aind-log-utils', and interpret the logs. Include examples and best practices for logging.

### Utilization of 'aind-log-utils':
- Use 'aind-log-utils' to initialize loggers in each module. Configure loggers to write to both console and file outputs.
- Leverage 'aind-log-utils' to define custom log formats and severity levels that align with the project's needs.
- Employ 'aind-log-utils' for structured logging where logs are formatted as JSON objects, making them easier to parse and analyze programmatically.

By following these steps and utilizing 'aind-log-utils' effectively, your application will become a valuable asset for researchers and developers working on complex projects within Code Ocean.

💬 Discussion Feed

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