axon-rag

v0.4.2 suspicious
6.0
Medium Risk

General-purpose open-source RAG engine with multi-LLM, hybrid retrieval, GraphRAG, and MCP support

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to its subprocess execution capabilities and potential for accessing sensitive credentials. While not conclusively malicious, these features warrant further scrutiny.

  • High shell risk due to subprocess execution
  • Potential credential harvesting from environment variables
Per-check LLM notes
  • Network: The use of urllib and httpx for network requests seems standard for making HTTP calls, but the GitHub login request is unusual and may indicate an unexpected feature or misuse.
  • Shell: Subprocess execution is risky as it can be used to run arbitrary commands. This could potentially lead to system compromise or unintended behavior.
  • Obfuscation: No obfuscation patterns detected.
  • Credentials: Potential risk of credential harvesting as the code accesses environment variables that could contain sensitive information.
  • Metadata: The maintainer has only one package, suggesting a potentially new or inactive account.

📦 Package Quality Overall: Medium (6.6/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/jyunming/Axon/tree/main/docs
  • Detailed PyPI description (13488 chars)
✦ High Contributing Guide 9.0

Has contribution guidelines and governance files

  • Governance file: governance.py
  • Governance file: governance.py
  • Contributing link: "Governance Console" -> https://github.com/jyunming/Axon/blob/main/docs/GOVERNANCE_C
◈ Medium Type Annotations 5.0

Partial type annotation coverage

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

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in jyunming/Axon
  • Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • urllib.request req = urllib.request.Request(url, method="GET") with urllib.request.urlop
  • l, method="GET") with urllib.request.urlopen(req, timeout=2.0) as resp: ok = 200 <= r
  • urllib.request with urllib.request.urlopen(tags_url, timeout=2.0) as resp: payload
  • import httpx resp = httpx.post( "https://github.com/login/device/code", jso
  • sh=True) token_resp = httpx.post( "https://github.com/login/oauth/access_token",
  • import httpx resp = httpx.get( "https://api.github.com/copilot_internal/v2/token",
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • h() try: result = subprocess.run( [*bash, command] if bash else command,
  • vsix_path} ...") result = subprocess.run( [code_cmd, "--install-extension", str(vsix_path)],
⚠ Credential Harvesting score 5.0

Found 2 credential access pattern(s)

  • .get("GITHUB_COPILOT_PAT") or os.environ.get( "GITHUB_TOKEN", "" ) # 2. Storage paths -- always der
  • y: self.api_key = os.getenv("API_KEY", os.getenv("OPENAI_API_KEY", "")) if not self.open
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository jyunming/Axon appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Open Source Contributor" 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 axon-rag 
Create a knowledge management mini-app called 'SmartQuery' that leverages the capabilities of the 'axon-rag' package to enhance user interaction with a diverse set of information sources. The app should allow users to ask complex questions about various topics and receive accurate, context-aware answers by utilizing a combination of retrieval augmentation generation (RAG), multiple large language models (LLMs), hybrid retrieval techniques, and GraphRAG functionalities.

Key Features:
1. User-friendly interface where users can input their queries.
2. Integration of multiple LLMs to provide more robust and versatile responses.
3. Hybrid retrieval mechanism combining both vector search and database querying for comprehensive data fetching.
4. Support for GraphRAG to handle complex queries that require understanding of relationships between entities.
5. Utilization of Multi-Client Processing (MCP) to manage multiple concurrent user sessions efficiently.
6. Ability to connect to different data sources such as local databases, cloud storages, and APIs.
7. Option for users to customize their preferred LLMs and retrieval settings.
8. Detailed logging and analytics for each query process.

How 'axon-rag' is Utilized:
- Use 'axon-rag' to configure and manage the integration of multiple LLMs within the app.
- Implement hybrid retrieval methods provided by 'axon-rag' to fetch relevant information from connected data sources.
- Leverage GraphRAG capabilities for handling intricate queries involving relational data.
- Apply MCP functionalities to ensure smooth operation during peak usage times.
- Customize the app's backend using 'axon-rag' to support various configurations based on user preferences and requirements.

💬 Discussion Feed

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