adaptive-profiler

v0.2.0 suspicious
6.0
Medium Risk

AutoML anomaly detection and schema-driven data quality for ETL pipelines

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows high risks related to credential handling and metadata integrity, suggesting potential security issues or malicious intent. However, it lacks direct evidence of exploitation.

  • High credential risk due to AWS credential access
  • Unusual commit patterns and obfuscation techniques
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require internet access.
  • Shell: No shell execution detected, indicating no direct system command execution from the package.
  • Obfuscation: The presence of pickling and unpickling operations without proper sanitization can indicate an attempt to obfuscate code or hide functionality, which poses a risk.
  • Credentials: The package accesses AWS credentials from various sources, which could be for legitimate use but also suggests potential unauthorized access or credential harvesting if not properly secured.
  • Metadata: The package shows signs of being newly created with unusual commit patterns, suggesting potential malicious intent.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • ) return pickle.loads(resp["Body"].read()) except ClientError as e:
✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 10.0

Found 4 credential access pattern(s)

  • "s3", region_name=os.getenv("AWS_REGION", "us-east-1"), aws_access_key_id=os.gete
  • aws_access_key_id=os.getenv("AWS_ACCESS_KEY_ID") or None, aws_secret_access_key=o
  • aws_secret_access_key=os.getenv("AWS_SECRET_ACCESS_KEY") or None, ) def save(
  • ential chain (env vars, ~/.aws/credentials, IAM role, etc.). """ def __init__(self, bucket: s
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 5.0

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
  • All 5 commits happened within 24 hours
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • 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 adaptive-profiler 
Create a Python-based mini-application named 'DataGuardian' that leverages the 'adaptive-profiler' package to ensure data integrity and detect anomalies within ETL (Extract, Transform, Load) processes. This application should serve as a robust tool for data engineers and analysts to monitor and maintain high-quality data throughout their workflows. Here’s a detailed breakdown of the project requirements and steps to implement it:

1. **Setup Environment**: Begin by setting up your Python environment. Ensure you have Python 3.x installed and create a virtual environment for your project. Install necessary packages including 'adaptive-profiler', pandas, and any other dependencies.

2. **Design the Architecture**: Design the architecture of your application focusing on modular components such as data ingestion, profiling, anomaly detection, and reporting. Each component should be encapsulated in its own module for better organization and maintainability.

3. **Data Ingestion Module**: Develop a module that supports various data sources (CSV, SQL databases, APIs). Implement functionality to read data from these sources and prepare it for analysis.

4. **Profiling Module**: Utilize the 'adaptive-profiler' package to automatically generate profiles of the ingested data. This includes schema validation, identifying data types, and summarizing statistical distributions. Ensure that the profiling process is efficient and scalable for large datasets.

5. **Anomaly Detection Module**: Integrate 'adaptive-profiler' to identify anomalies in the data based on the generated profiles. This could include detecting outliers, missing values, inconsistencies, and unexpected patterns. The module should provide actionable insights and recommendations for correcting anomalies.

6. **Reporting Module**: Create a reporting feature that summarizes the findings from the profiling and anomaly detection modules. Reports should be easy to understand and customizable. Include options for generating visualizations using libraries like matplotlib or seaborn.

7. **User Interface**: Develop a simple command-line interface (CLI) for interacting with the application. Users should be able to specify data sources, run analyses, and view reports through the CLI.

8. **Testing & Documentation**: Write comprehensive tests for each module to ensure reliability and accuracy. Document your code and provide a user guide detailing how to install and use DataGuardian effectively.

9. **Deployment**: Prepare the application for deployment by packaging it into a distributable format (e.g., using PyInstaller for standalone executables). Consider hosting documentation and examples online for broader accessibility.

Throughout the development process, focus on leveraging 'adaptive-profiler' to automate complex tasks related to data quality and anomaly detection, thereby reducing manual effort and increasing efficiency in data management.