🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits a moderate network risk due to missing SSL verification in some network calls, despite having low risks in other categories. The maintainer's single package history adds to the suspicion.

moderate network risk due to unverified SSL connections
maintainer has only one package

Per-check LLM notes

Network: The observed network calls are typical for authentication and API interactions but lack SSL verification in some cases, which could be a security risk.
Shell: No shell execution patterns were detected.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer has only one package, indicating a new or less active account, which raises some suspicion but not conclusive evidence of malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

access_token_response = requests.post(token_url, data=json.dumps(data), headers=headers, verify=Fa
api_call_response = requests.post(api_url, headers=api_call_headers, data=json.dumps(data), ve
api_call_response = requests.get(api_url, headers=api_call_headers, data=None, verify=verify)
y: response = requests.put(presigned_url, data=file, headers=put_headers, verify=False)
dex("Date") url = requests.get(resp['ts'], verify=verify) ts = pd.read_csv(io.S
y=False) ports_data = requests.get(ports_url, verify=False) ports_df = pd.read_csv(io.S

✓ Code Obfuscation

No obfuscation patterns detected

✓ 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: sura-im.com

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Daniel Velasquez" 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 PyQuantimClient

Your task is to develop a simple yet powerful mini-application using the 'PyQuantimClient' Python package, which allows you to interact with quantIM services. This application will serve as a user-friendly interface to perform various operations such as querying information, sending requests, and managing data through quantIM's API endpoints. Here’s a step-by-step guide on how to approach this project:

1. **Project Setup**: Begin by setting up your development environment. Ensure Python is installed, along with any necessary packages like PyQuantimClient, requests, and Flask for web integration if needed.

2. **Understanding PyQuantimClient**: Study the documentation of PyQuantimClient to understand its functionalities, such as authentication methods, available API endpoints, and response formats. This understanding will be crucial for effectively utilizing the package.

3. **Application Design**: Design your application to include the following core features:
- User Authentication: Implement a secure method for users to authenticate themselves before accessing quantIM services. This could involve OAuth2 or other industry-standard authentication protocols supported by PyQuantimClient.
- Query Interface: Develop an interface where users can input queries to fetch specific data from quantIM services. Ensure the application supports both simple and complex query structures.
- Data Management: Allow users to manage their data by providing options to update, delete, and retrieve information efficiently.
- Real-Time Notifications: Integrate real-time notifications for important updates or changes in the data fetched from quantIM services.

4. **Implementation**: Start coding! Use PyQuantimClient to handle all interactions with quantIM services. Make sure to implement error handling and logging mechanisms to ensure robustness.

5. **Testing**: Rigorously test your application under different scenarios to ensure it works as expected. Pay special attention to edge cases and potential security vulnerabilities.

6. **Deployment**: Once testing is successful, deploy your application. Consider deploying it as a web service for broader accessibility.

7. **Documentation**: Provide comprehensive documentation for your application, detailing how to install, configure, and use it effectively.

By following these steps, you’ll create a valuable tool that leverages the power of PyQuantimClient to interact seamlessly with quantIM services.