aaiclick

v0.0.16 suspicious
5.0
Medium Risk

A Python framework that translates Python code into ClickHouse operations for big data computing

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits elevated shell risk due to potential system-level modifications via 'claude' and 'mcp', while other risks remain low. Given the new maintainer account and low package activity, there is a possibility of supply-chain compromise.

  • Elevated shell risk from executing commands related to 'claude' and 'mcp'
  • Low activity and new maintainer account suggest potential supply-chain attack risk
Per-check LLM notes
  • Network: The network patterns detected indicate standard HTTP/HTTPS handling, which is common and not inherently suspicious.
  • Shell: Executing shell commands to interact with 'claude' and 'mcp' suggests potential system-level modifications, which could be risky depending on the context and permissions granted.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The low activity and new maintainer account suggest potential risk, but no clear indicators of malicious intent.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • lock exits. The default ``urllib.request.urlopen`` raises ``HTTPError`` from inside the error pro
  • rning``. """ opener = urllib.request.OpenerDirector() opener.add_handler(urllib.request.HTTPH
  • ctor() opener.add_handler(urllib.request.HTTPHandler()) opener.add_handler(urllib.request.HTTPSHa
  • ler()) opener.add_handler(urllib.request.HTTPSHandler()) opener.add_handler(urllib.request.HTTPDe
  • ler()) opener.add_handler(urllib.request.HTTPDefaultErrorHandler()) with opener.open(url) as resp
  • fetch happens in Python via ``urllib.request.urlretrieve`` (see ``chdb_client._rewrite_external_url
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

  • ).expanduser()) result = subprocess.run(["which", "claude"], capture_output=True) if result.retu
  • ")) try: subprocess.run( [claude_cmd, "mcp", "remove", server_name],
  • ass try: subprocess.run( [claude_cmd, "mcp", "add-json", server_name
✓ 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 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "aaiclick Contributors" 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 aaiclick 
Your task is to develop a small yet powerful command-line tool using the 'aaiclick' package, which allows you to interact with ClickHouse databases seamlessly through Python. This tool will enable users to perform various operations on their ClickHouse databases, including but not limited to inserting new records, querying data, updating existing entries, and deleting records. Here’s a detailed guide on how to proceed:

1. **Project Setup**: Start by setting up your development environment. Ensure you have Python installed along with the 'aaiclick' package. If 'aaiclick' is not yet available in PyPI, consider cloning its repository from GitHub and installing it locally.

2. **Application Design**: Your application should be designed as a command-line interface (CLI). Users should be able to execute commands like 'insert', 'query', 'update', and 'delete'. Each of these commands should accept parameters specific to the operation being performed.

3. **Implementation Details**:
   - **Insert Command**: Implement a function that takes input data from the user and inserts it into a specified table in the ClickHouse database. Use 'aaiclick' to convert this Python operation into the appropriate ClickHouse SQL command.
   - **Query Command**: Allow users to query data based on certain conditions. The output should be formatted nicely for easy readability.
   - **Update Command**: Provide functionality to update existing records in the database. This command should allow specifying both the condition for the update and the new values.
   - **Delete Command**: Implement a delete feature that allows users to remove records based on specific criteria.

4. **Error Handling**: Make sure your application gracefully handles errors such as incorrect inputs, connection issues, and database-specific errors.

5. **Testing**: Write tests to ensure each command works as expected under different scenarios. Consider edge cases where the database might be empty or where network issues could occur.

6. **Documentation**: Finally, document your application thoroughly. Include a README file explaining how to install and use the application, along with examples of common usage patterns.

By following these steps, you'll create a robust CLI tool that leverages 'aaiclick' to simplify interactions with ClickHouse databases.