ariasql

v1.0.1 suspicious
5.0
Medium Risk

AriaSQL — Agentic SQL Agent. Natural language to SQL with ReAct reasoning, semantic cache, and a bundled React UI.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits a moderate level of risk due to its high obfuscation risk and uncertain author metadata, despite having low risks in other categories.

  • High obfuscation risk indicating potential for hidden malicious activities
  • Sparse and potentially suspicious author metadata
Per-check LLM notes
  • Network: The package appears to make network calls which could be for legitimate purposes like logging or reporting usage statistics, but further investigation is needed to confirm the intent.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: The observed pattern suggests an attempt to obfuscate code which could be used for hiding logic or malicious activities.
  • Credentials: No clear evidence of credential harvesting is present based on the provided snippet.
  • Metadata: The author's information is sparse and the account seems new or inactive, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (5.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. test_improvements.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/thepradip/AriaSQL#readme
  • Detailed PyPI description (4658 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 209 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 100 commits in thepradip/AriaSQL
  • Single author but highly active (100 commits)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

  • }).encode() req = urllib.request.Request( self._webhook, data=payload,
  • T", ) urllib.request.urlopen(req, timeout=5) except Exception as e:
  • nstall httpx") resp = httpx.post( f"{self._base_url}/api/chat", json=
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • loat( __import__("re").search(r"[\d.]+", line.split(":")[-1]).group()
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ 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

Repository thepradip/AriaSQL appears legitimate

⚠ 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 ariasql 
Create a web-based mini-application using Python's 'ariasql' package that enables users to interact with a database through natural language queries. The application should feature a user-friendly interface where users can input their questions or commands in plain English, and the system will translate these into appropriate SQL queries to retrieve or modify data from a database. Here’s a detailed breakdown of the project steps and features:

1. **Setup Environment**: Begin by setting up your development environment. Ensure you have Python installed along with Flask for the backend and React for the frontend. Install 'ariasql' via pip.
2. **Database Integration**: Integrate a sample database (e.g., SQLite) into your application. Populate it with some test data relevant to the application's purpose (e.g., customer information, product details).
3. **Natural Language Interface**: Develop a feature that allows users to enter natural language queries about the data in the database. For example, a user might ask, “What are the total sales for each product?”
4. **Query Parsing and Execution**: Use 'ariasql' to parse the natural language inputs into executable SQL queries. Implement the ReAct reasoning capabilities of 'ariasql' to handle complex queries and ensure accurate translations.
5. **Semantic Cache Implementation**: Utilize the semantic cache feature of 'ariasql' to store query results for faster retrieval when similar queries are made again, enhancing performance.
6. **React UI Development**: Create a React-based user interface for the application. This UI should allow users to submit queries and display the results in a readable format. Consider adding features like a history of previous queries, error messages for incorrect inputs, and a live preview of SQL translation.
7. **Testing and Validation**: Thoroughly test the application to ensure it correctly translates natural language into SQL and accurately retrieves or modifies data based on the input. Validate the application against various types of queries to ensure robustness.
8. **Deployment**: Deploy your application on a platform like Heroku or AWS so that it can be accessed online.

The goal of this project is not only to demonstrate the capabilities of 'ariasql' but also to create a practical tool that simplifies database interaction for non-technical users. Emphasize usability, efficiency, and accuracy throughout the development process.

💬 Discussion Feed

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