argo-proxy

v3.0.4 suspicious
6.0
Medium Risk

Proxy server to Argo API, OpenAI format compatible

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits several concerning behaviors including high shell and obfuscation risks, which may indicate malicious intent. However, without concrete evidence of malicious activity, it cannot be conclusively labeled as malicious.

  • High shell execution risk
  • Significant obfuscation practices
Per-check LLM notes
  • Network: Network calls suggest legitimate HTTP requests, possibly for proxy operations.
  • Shell: Shell execution indicates potential local file manipulation or command execution, which could be risky if not properly controlled.
  • Obfuscation: The presence of base64 decoding suggests potential obfuscation of sensitive data, raising concern.
  • Credentials: The use of an environment variable for an API key is common but the lack of context around its usage could indicate improper handling or harvesting.
  • Metadata: The maintainer has a new or inactive account and lacks a proper author name, which raises some suspicion.

📦 Package Quality Overall: Medium (5.6/10)

✦ High Test Suite 9.0

Test suite present — 8 test file(s) found

  • 8 test file(s) detected (e.g. test_chat_completions.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/Oaklight/argo-openai-proxy#readme
  • Detailed PyPI description (13256 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

  • 379 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 100 commits in Oaklight/argo-openai-proxy
  • Single author but highly active (100 commits)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • } try: async with aiohttp.ClientSession() as session: async with session.get(
  • try: async with aiohttp.ClientSession( connector=connector, timeout=client
  • solver) >>> session = aiohttp.ClientSession(connector=connector) """ def __init__( self
  • d: self.session = aiohttp.ClientSession( connector=self.connector, t
  • try: async with aiohttp.ClientSession( connector=connector, timeou
  • try: async with aiohttp.ClientSession( connector=connector, timeout=aiohtt
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • e("data:", "") img_data = base64.b64decode(b64_data) return (img_data, media_type) async def _dow
⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • try: subprocess.run([editor, path], check=True) return
  • }") print() result = subprocess.run(cmd) if result.returncode != 0: print("Error: Up
⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • //localhost:44498") API_KEY = os.getenv("API_KEY", "whatever+random") @pytest.fixture(scope="module") def
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmx.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Oaklight/argo-openai-proxy 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 argo-proxy 
Create a conversational AI chatbot application named 'ArgoChat' using the Python package 'argo-proxy'. This application will serve as a bridge between users and the Argo API, allowing users to interact with advanced natural language processing capabilities in a conversational manner. The app should be designed to be user-friendly, with a simple command-line interface for ease of use during development and testing phases. Additionally, it should support basic conversation history tracking to enhance user experience.

Key Features:
1. User Authentication: Implement a simple authentication mechanism to ensure only registered users can access the chatbot.
2. Real-time Conversations: Users should be able to start a conversation and receive responses in real-time.
3. History Tracking: Maintain a log of conversations for each user, allowing them to review past interactions.
4. Customizable Responses: Allow users to provide feedback on the quality of responses, which can then be used to improve future interactions.
5. Command Line Interface: Provide a command-line interface where users can initiate conversations and view their interaction history.
6. Error Handling: Implement robust error handling to manage issues like network failures or invalid input gracefully.

How to Use 'argo-proxy':
- Utilize 'argo-proxy' to establish a connection to the Argo API. This package acts as a proxy server, ensuring compatibility with the OpenAI API format, making it easier to integrate Argo's advanced NLP services into your application.
- For each user request, route the request through 'argo-proxy' to fetch a response from the Argo API. Ensure that the request and response formats adhere to the OpenAI API standards for seamless integration.
- Use the proxy to manage multiple simultaneous connections efficiently, supporting a scalable solution as the number of users grows.
- Leverage 'argo-proxy' to handle any necessary transformations of data between the client and the Argo API, maintaining the integrity and security of user interactions.

💬 Discussion Feed

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