attune-ai

v7.4.0 suspicious
6.0
Medium Risk

AI-powered developer workflows for Claude with cost optimization, multi-agent orchestration, and workflow automation.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits high obfuscation risk due to the use of eval() on untrusted inputs, which poses significant security risks. Despite no direct evidence of malicious intent, the incomplete maintainer metadata raises concerns about the package's trustworthiness.

  • High obfuscation risk due to eval() usage
  • Incomplete maintainer metadata
Per-check LLM notes
  • Network: No network calls detected, which is normal and expected.
  • Shell: Shell executions are for code formatting and linting purposes, indicating standard development practices.
  • Obfuscation: The use of eval() on untrusted input is highly risky and can lead to arbitrary code execution.
  • Credentials: No direct evidence of credential harvesting patterns, but potential misuse cannot be ruled out without further investigation.
  • Metadata: The maintainer's author information is incomplete and they may be new or inactive, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 2 test file(s) found

  • Test runner config found: pyproject.toml
  • 2 test file(s) detected (e.g. test_approval_gates.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://www.smartaimemory.com/framework-docs/
  • Detailed PyPI description (17347 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 139 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in Smart-AI-Memory/attune-ai
  • Two distinct contributors found

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • user_input(data):\n return eval(data)", ) # Check which model was used if respo
⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

  • f --fix...") result = subprocess.run( ["ruff", "check", project_path, "--fix", "--exi
  • format...") result = subprocess.run( ["ruff", "format", project_path], c
  • g isort...") result = subprocess.run( ["isort", project_path, "--profile", "black"],
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: smartaimemory.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Smart-AI-Memory/attune-ai 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 attune-ai 
Create a Python-based mini-application called 'AIWorkflowOptimizer' that leverages the 'attune-ai' package to streamline and optimize developer workflows. This application will be designed to automate repetitive tasks, manage multiple AI agents efficiently, and reduce costs associated with cloud services. Here are the key functionalities and steps to develop this application:

1. **Task Automation**: Integrate the ability to schedule and execute routine tasks such as code formatting, linting, testing, and deployment through the 'attune-ai' package.
2. **Multi-Agent Orchestration**: Use 'attune-ai' to manage a fleet of AI agents that handle different aspects of the development process, ensuring seamless collaboration between them.
3. **Cost Optimization**: Implement a feature within the application that monitors and optimizes the usage of cloud resources, minimizing costs while maintaining performance standards.
4. **User Interface**: Develop a simple command-line interface (CLI) for users to interact with the application, allowing them to configure workflows, monitor progress, and receive notifications.
5. **Integration with Existing Tools**: Ensure the application can integrate smoothly with popular development tools and platforms like GitHub, GitLab, and Docker.

The 'attune-ai' package plays a central role in enabling these functionalities by providing advanced AI-driven capabilities tailored for developers. Utilize its APIs and modules to orchestrate complex workflows, manage resources intelligently, and facilitate communication between various components of the system. Your task is to design and implement a fully functional version of 'AIWorkflowOptimizer', demonstrating the power and flexibility of 'attune-ai' in enhancing developer productivity.

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