🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to potential obfuscation techniques and lack of community engagement, raising concerns about its legitimacy and safety.

High obfuscation risk
Low metadata quality

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires network interaction for its functionality.
Shell: No shell execution patterns detected, indicating low risk of command injection or similar attacks.
Obfuscation: The use of base64 encoding for strings can be indicative of obfuscation to hide code logic or data, potentially malicious.
Credentials: No direct credential harvesting patterns detected, but the presence of obfuscation could indicate an attempt to conceal such activities.
Metadata: The repository is new, lacks community engagement, and the maintainer shows low effort in package management.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

urn None if text is None else base64.b64decode(text.encode("ascii")).decode("utf-8") def kql_replace_s

✓ 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 score 5.0

Git history flags: Repository created very recently: 5 day(s) ago (2026-06-01T10:12:46Z)

Repository created very recently: 5 day(s) ago (2026-06-01T10:12:46Z)
Repository has zero stars and zero forks

⚠ 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 adxpandas

Create a data analysis tool using Python that leverages the 'adxpandas' package to execute Kusto Query Language (KQL) queries on pandas DataFrames. This tool will serve as a bridge between the powerful querying capabilities of KQL and the ease of use provided by pandas, making it easier for users to analyze large datasets stored in pandas DataFrames.

Step 1: Setup your environment
- Install Python and necessary libraries including pandas and adxpandas.
- Ensure you have a sample dataset suitable for analysis, such as financial records, web logs, or sensor data.

Step 2: Design the User Interface
- Develop a simple command-line interface (CLI) where users can input their KQL queries.
- Implement basic error handling for invalid inputs or query errors.

Step 3: Integrate adxpandas
- Use adxpandas to convert the user's input KQL queries into operations that can be executed on pandas DataFrames.
- Execute these operations on the provided dataset and display the results back to the user.

Step 4: Enhance Functionality
- Add support for loading different types of datasets (CSV, Excel, SQL databases).
- Include functionality for saving query results to files or exporting them to other formats.
- Provide examples of common KQL queries that users can run out-of-the-box.

Step 5: Testing and Documentation
- Test the tool with various datasets and KQL queries to ensure reliability and accuracy.
- Document the setup process, usage instructions, and a guide on how to write effective KQL queries for pandas DataFrames.

By following these steps, you'll create a versatile data analysis tool that simplifies complex data querying tasks, making it accessible to both beginners and experienced data analysts.