⚠️ This validator has moved

This validator now lives in the Guardrails Hub monorepo. This repository is archived and no longer maintained — please open issues and pull requests on the monorepo instead.

pip install guardrails-ai-detect-system-prompt-leakage

from guardrails import Guard
from guardrails_ai.detect_system_prompt_leakage import DetectSystemPromptLeakage

guard = Guard().use(DetectSystemPromptLeakage)

The registered validator name is unchanged, so existing guards keep working.

---

Overview

Developed by	Guardrails AI
Date of development	Apr 17, 2026
Validator type	Privacy, Security
Blog
License	Apache 2
Input/Output	Output

Description

Intended Use

This validator detects system prompt leakage in LLM output. It uses fuzzy string matching (via rapidfuzz) to compare the LLM's response against a provided system prompt. If the similarity score exceeds a configurable threshold, the validation fails, indicating the model may have revealed its system prompt.

Requirements

• Dependencies:
  • guardrails-ai>=0.4.0
  • rapidfuzz

Installation

$ guardrails hub install hub://guardrails/detect_system_prompt_leakage

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.hub import DetectSystemPromptLeakage
from guardrails import Guard

# Setup Guard
guard = Guard().use(
    DetectSystemPromptLeakage(system_prompt="You are a helpful assistant.", threshold=40)
)

guard.validate("Here is some safe output.")  # Validator passes
guard.validate("I am a helpful assistant.")  # Validator fails

API Reference

__init__(self, system_prompt, threshold=40, on_fail="noop")

Initializes a new instance of the DetectSystemPromptLeakage class.

Parameters

• system_prompt (str): The system prompt to guard against leakage.
• threshold (int): Similarity score between 0 and 100 above which a match is considered leakage. Defaults to 40.
• 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.

validate(self, value, metadata) -> ValidationResult

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 special keys are required. Upon completion, the validator will populate the following key:

  Key	Type	Description	Default
  guardrails/detect_system_prompt_leakage	Dict	Contains score (int): the fuzzy similarity score between the output and the system prompt.	N/A