ai-intervention-agent

v1.7.9 suspicious
7.0
High Risk

AI Intervention Agent: MCP server enabling real-time user intervention in AI-assisted development workflows.

πŸ€– AI Analysis

Final verdict: SUSPICIOUS

The package exhibits high risks associated with network and shell execution, along with moderate obfuscation. These factors, combined with unreliable metadata, suggest potential malicious intent, though direct evidence of harmful activity is lacking.

  • High network risk due to httpx usage
  • Significant shell execution risk
  • Moderate obfuscation indicating possible hidden functionality
Per-check LLM notes
  • Network: The use of httpx for making network calls is not inherently malicious but could be used for unexpected activities if the target URLs are controlled by an external entity.
  • Shell: Execution of shell commands can pose significant risks if the commands are not properly sanitized or controlled, potentially leading to unauthorized actions on the system.
  • Obfuscation: The code snippet shows signs of obfuscation using base64 encoding, which could be used to hide the true functionality of the code.
  • Credentials: No clear patterns of credential harvesting were detected.
  • Metadata: The author's information is incomplete and the maintainer has few credentials on PyPI, suggesting potential unreliability.

πŸ“¦ Package Quality Overall: Medium (5.0/10)

β—‹ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
β—ˆ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/xiadengma/ai-intervention-agent#readme
  • Detailed PyPI description (19681 chars)
β—‹ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
β—ˆ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 305 type-annotated function signatures detected in source
β—ˆ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in xiadengma/ai-intervention-agent
  • Two distinct contributors found

πŸ”¬ Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

  • ies=3) self.session = httpx.Client( transport=transport, headers={
  • ηš„ worst case。 resp = httpx.get(target_url, timeout=0.5) except Exception as net_exc:
  • import httpx resp = httpx.get(target_url, timeout=0.5) except Exception as net_exc:
  • ies) client = httpx.AsyncClient( transport=transport,
  • ies) client = httpx.Client( transport=transport,
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • ppet) % 4) % 4) raw = base64.b64decode(snippet, validate=False) mime_signatures = [
⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • rgs)}") process = subprocess.Popen( args, stdout=subprocess.DEV
  • ι‡ε―ζ—ΆζŠŠ IDE δΉŸεΈ¦θ΅°γ€‚ subprocess.Popen( cmd, stdin=subproce
βœ“ Credential Harvesting

No credential harvesting patterns detected

βœ“ Typosquatting

No typosquatting candidates detected

βœ“ Registered Email Domain

Email domain looks legitimate: users.noreply.github.com>

βœ“ Suspicious Page Links

All external links appear legitimate

βœ“ Git Repository History

Repository xiadengma/ai-intervention-agent 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)
βœ“ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

πŸ’‘ AI App Starter Prompt

Use this prompt to build a project with ai-intervention-agent 
Create a real-time code review tool named 'CodeGuardian' using the 'ai-intervention-agent' Python package. This tool will enable developers to work more efficiently by allowing them to intervene in real-time during code generation or editing processes facilitated by AI. CodeGuardian should have the following functionalities:

1. **Real-Time Code Analysis**: Integrate with popular code editors like VSCode or Sublime Text to provide real-time feedback on code quality, suggesting improvements as the developer types.
2. **User Intervention**: Allow users to manually correct or modify suggestions provided by the AI, ensuring the final output aligns with their specific coding standards and preferences.
3. **History Tracking**: Maintain a history of changes made both by the AI and the user, facilitating easy tracking of modifications and discussions about different approaches.
4. **Integration with Version Control Systems**: Support integration with Git to automatically push reviewed and improved code snippets back into the repository.
5. **Customizable Settings**: Provide options for users to customize settings such as preferred coding styles, common errors to watch out for, and the level of AI assistance they want.

To achieve these goals, you'll need to utilize the 'ai-intervention-agent' package as follows:
- Use its MCP server capabilities to set up a communication channel between the code editor and your application.
- Implement the real-time analysis feature by leveraging the package’s ability to intervene in AI-generated content, allowing for immediate feedback and adjustments.
- Ensure that the user intervention aspect is seamless and intuitive, making it easy for developers to guide the AI towards the desired outcome.
- For history tracking, use the package's logging features to record all interventions and modifications.
- Lastly, explore the package’s extensibility to integrate with version control systems, ensuring that the reviewed code is seamlessly integrated back into the project workflow.

This project aims to bridge the gap between human creativity and AI efficiency, offering a powerful tool for modern software development.