SecureMorph

SecureMorph is a Python package designed to process and validate text inputs related to advanced security protocols, such as secrets erasure upon observation. It utilizes structured pattern matching with regex to interpret user instructions, verify system behaviors—including formal verification results—and produce clear, structured summaries of the security mechanisms involved. This tool enables organizations to automate the validation, documentation, and analysis of complex security models, ensuring sensitive information remains protected under specified observable conditions through verified and repeatable procedures.

Installation

Install SecureMorph via pip:

pip install SecureMorph

Usage

Here's an example of how to use SecureMorph in your Python projects:

from SecureMorph import SecureMorph

# Example input string
user_input = "Verify that secrets are erased when observed."

# Call the function
result = SecureMorph(user_input)

# Output the extracted data or verification results
print(result)

Customizing the Language Model

SecureMorph defaults to using the ChatLLM7 model from langchain_llm7, which can be installed from PyPI. You can also pass your own LLM instance if desired:

from langchain_llm7 import YourCustomLLM

my_llm = YourCustomLLM()
result = SecureMorph(user_input, llm=my_llm)

API Keys and Rate Limits

For higher rate limits or access to the LLM7 API, you can:

• Set your API key as an environment variable LLM7_API_KEY.
• Or pass it directly in the function call:

result = SecureMorph(user_input, api_key="your_api_key_here")

You can obtain a free API key by registering at https://token.llm7.io/.

Configuration

• The default LLM is ChatLLM7 from langchain_llm7.
• You may specify a different LLM instance for custom integrations.
• The API key must be provided for API access if not using the default setup.

Issues

For bugs, feature requests, or support, please open an issue on GitHub:
https://github.com/yourusername/SecureMorph/issues

Author

Eugene Evstafev
Email: hi@euegne.plus

License

This project is licensed under the MIT License.