api-for-gemini

v1.3.3 suspicious
7.0
High Risk

Gemini API proxy for routing requests to Google or OpenAI-compatible backends

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits unusually high shell execution risk and incomplete metadata, which together suggest potential malicious intent despite lacking clear signs of obfuscation or credential theft.

  • High shell risk (9/10) indicating possible execution of external commands with elevated privileges.
  • Incomplete author metadata and potentially new/inactive account.
Per-check LLM notes
  • Network: The network call pattern suggests normal HTTP GET request behavior, possibly for API interaction.
  • Shell: The shell execution patterns indicate the package may execute external commands with elevated privileges, which is highly suspicious and could be indicative of malicious activity.
  • Obfuscation: No obfuscation patterns detected, suggesting low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author's information is incomplete and the account seems new or inactive, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (3.8/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (5539 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 30 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 57 commits in WuJunkai2004/api-for-gemini
  • Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • g.""" try: conn = http.client.HTTPConnection(host, port, timeout=timeout) conn.request("GET", "/s
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • 0000 | 0x00000008 subprocess.Popen( cmd, creationflags=creation
  • ate a new session subprocess.Popen( cmd, preexec_fn=os.setsid,
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

⚠ Registered Email Domain score 3.0

Suspicious email domain flags: Very short email domain: qq.com>

  • Very short email domain: qq.com>
✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository WuJunkai2004/api-for-gemini 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 api-for-gemini 
Create a conversational assistant named 'GeminiBot' that leverages the 'api-for-gemini' package to dynamically route user queries to either Google Search or OpenAI's text generation models based on the nature of the query. GeminiBot should be able to understand the context of a user's question and intelligently decide which backend service would provide the most relevant response. Here’s a step-by-step guide to building GeminiBot:

1. **Setup Project Environment**: Begin by setting up a Python virtual environment and installing necessary packages including 'api-for-gemini', Flask for web framework, and any other required libraries.
2. **Integrate api-for-gemini**: Use the 'api-for-gemini' package to set up the backend routing logic. This involves configuring the API client to communicate with both Google and OpenAI services.
3. **Design User Interface**: Develop a simple web interface using Flask where users can input their questions or prompts. The UI should include a text box for input and a submit button.
4. **Implement Contextual Routing Logic**: Implement a function that analyzes the user's input to determine whether it is more suitable for a search query or a creative text generation task. For instance, if the user asks a factual question, route it to Google; if they request a poem or story, send it to OpenAI.
5. **Handle Responses**: Once the appropriate backend has processed the request, ensure that the responses are correctly formatted and displayed back to the user through the web interface.
6. **Enhance User Experience**: Consider adding features such as saving previous conversations, allowing users to switch between different backends manually, and providing an option to rate the relevance of the responses.
7. **Testing and Debugging**: Thoroughly test GeminiBot with various types of inputs to ensure accurate routing and reliable performance. Pay special attention to handling errors gracefully and providing meaningful feedback to the user.
8. **Deployment**: Deploy your application to a cloud platform like Heroku or AWS so it can be accessed online.

By following these steps, you'll create a versatile and intelligent conversational assistant that can adapt its behavior based on the user's needs, all powered by the dynamic capabilities of 'api-for-gemini'.

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