Guardrails AI
Secrets Present
Detects the secrets present in text by matching against common patterns for API keys and other sensitive information.
en
string
code
Rule
ML
Data Leakage
Chatbots
CodeGen
Text2SQL

Overview

updated 2 months
Developed by:
Guardrails AI
Date of development:
Feb 15, 2024
Validator type:
Safety
Blog:
License:
Apache 2
Input/Output:
Input, Output

Playground

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Description
Intended Use

This validator monitors any text (input or output) and detects secrets present in the text. Under-the-hood, the validator uses the detect-secrets library to check whether the text contains any secrets. If any secrets are detected, the validator fails and returns the text with the secrets replaced with asterisks. Otherwise, the validator returns the generated code snippet.

Requirements
  • Dependencies:
    • guardrails-ai>=0.4.0
    • detect-secrets
Installation
$ guardrails hub install hub://guardrails/secrets_present
Usage Examples
Validating string output via Python

In this example, we apply the validator to a string output generated by an LLM.

# Import Guard and Validator
from guardrails import Guard
from guardrails.hub import SecretsPresent

# Setup Guard
guard = Guard().use(
    SecretsPresent(on_fail="exception")
)

response = guard.validate(
    """
    def hello():
        name = "James"
        age = 25
        return {"name": name, "age": age}
    """
)  # Validator passes

try:
    response = guard.validate(
        """
        def hello():
            user_id = "1234"
            user_pwd = "password1234"
            user_api_key = "sk-xhdfgtest"
        """
    )  # Validator fails
except Exception as e:
    print(e)

Output:

Validation failed for field with errors: The following secrets were detected in your response:
password1234
sk-xhdfgtest
API Reference

__init__(self, on_fail="noop")

Initializes a new instance of the Validator class.

Parameters

  • on_fail (str, Callable): The policy to enact when a validator fails. If str, must be one of reask, fix, filter, refrain, noop, exception or fix_reask. Otherwise, must be a function that is called when the validator fails.

__call__(self, value, metadata={}) -> ValidationOutcome

Validates the given value using the rules defined in this validator, relying on the metadata provided to customize the validation process. This method is automatically invoked by guard.parse(...), ensuring the validation logic is applied to the input data.

Note:

  1. This method should not be called directly by the user. Instead, invoke guard.parse(...) where this method will be called internally for each associated Validator.
  2. When invoking guard.parse(...), ensure to pass the appropriate metadata dictionary that includes keys and values required by this validator. If guard is associated with multiple validators, combine all necessary metadata into a single dictionary.

Parameters

  • value (Any): The input value to validate.
  • metadata (dict): A dictionary containing metadata required for validation. No additional metadata keys are needed for this validator.