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import asyncio
from typing import Any
import weave
from guardrails import Guard
from guardrails.hub import SecretsPresent
from llm_guard.input_scanners import Secrets
from llm_guard.util import configure_logger
from guardrails_genie.guardrails import GuardrailManager
from guardrails_genie.guardrails.base import Guardrail
from guardrails_genie.guardrails.secrets_detection import (
SecretsDetectionResponse,
SecretsDetectionSimpleResponse,
SecretsDetectionGuardrail,
)
from guardrails_genie.metrics import AccuracyMetric
logger = configure_logger(log_level="ERROR")
class GuardrailsAISecretsDetector(Guardrail):
"""
A class to detect secrets using Guardrails AI.
Attributes:
validator (Any): The validator used for detecting secrets.
"""
validator: Any
def __init__(self):
"""
Initializes the GuardrailsAISecretsDetector with a validator.
"""
validator = Guard().use(SecretsPresent, on_fail="fix")
super().__init__(validator=validator)
def scan(self, text: str) -> dict:
"""
Scans the given text for secrets.
Args:
text (str): The text to scan for secrets.
Returns:
dict: A dictionary containing the scan results.
"""
response = self.validator.validate(text)
if response.validation_summaries:
summary = response.validation_summaries[0]
return {
"has_secret": True,
"detected_secrets": {
str(k): v
for k, v in enumerate(
summary.failure_reason.splitlines()[1:], start=1
)
},
"explanation": summary.failure_reason,
"modified_prompt": response.validated_output,
"risk_score": 1.0,
}
else:
return {
"has_secret": False,
"detected_secrets": None,
"explanation": "No secrets detected in the text.",
"modified_prompt": response.validated_output,
"risk_score": 0.0,
}
@weave.op
def guard(
self,
prompt: str,
return_detected_secrets: bool = True,
**kwargs,
) -> SecretsDetectionResponse | SecretsDetectionResponse:
"""
Guards the given prompt by scanning for secrets.
Args:
prompt (str): The prompt to scan for secrets.
return_detected_secrets (bool): Whether to return detected secrets.
Returns:
SecretsDetectionResponse | SecretsDetectionSimpleResponse: The response after scanning for secrets.
"""
results = self.scan(prompt)
if return_detected_secrets:
return SecretsDetectionResponse(
contains_secrets=results["has_secret"],
detected_secrets=results["detected_secrets"],
explanation=results["explanation"],
redacted_text=results["modified_prompt"],
risk_score=results["risk_score"],
)
else:
return SecretsDetectionSimpleResponse(
contains_secrets=not results["has_secret"],
explanation=results["explanation"],
redacted_text=results["modified_prompt"],
risk_score=results["risk_score"],
)
class LLMGuardSecretsDetector(Guardrail):
"""
A class to detect secrets using LLM Guard.
Attributes:
validator (Any): The validator used for detecting secrets.
"""
validator: Any
def __init__(self):
"""
Initializes the LLMGuardSecretsDetector with a validator.
"""
validator = Secrets(redact_mode="all")
super().__init__(validator=validator)
def scan(self, text: str) -> dict:
"""
Scans the given text for secrets.
Args:
text (str): The text to scan for secrets.
Returns:
dict: A dictionary containing the scan results.
"""
sanitized_prompt, is_valid, risk_score = self.validator.scan(text)
if is_valid:
return {
"has_secret": not is_valid,
"detected_secrets": None,
"explanation": "No secrets detected in the text.",
"modified_prompt": sanitized_prompt,
"risk_score": risk_score,
}
else:
return {
"has_secret": not is_valid,
"detected_secrets": {},
"explanation": "This library does not return detected secrets.",
"modified_prompt": sanitized_prompt,
"risk_score": risk_score,
}
@weave.op
def guard(
self,
prompt: str,
return_detected_secrets: bool = True,
**kwargs,
) -> SecretsDetectionResponse | SecretsDetectionResponse:
"""
Guards the given prompt by scanning for secrets.
Args:
prompt (str): The prompt to scan for secrets.
return_detected_secrets (bool): Whether to return detected secrets.
Returns:
SecretsDetectionResponse | SecretsDetectionSimpleResponse: The response after scanning for secrets.
"""
results = self.scan(prompt)
if return_detected_secrets:
return SecretsDetectionResponse(
contains_secrets=results["has_secret"],
detected_secrets=results["detected_secrets"],
explanation=results["explanation"],
redacted_text=results["modified_prompt"],
risk_score=results["risk_score"],
)
else:
return SecretsDetectionSimpleResponse(
contains_secrets=not results["has_secret"],
explanation=results["explanation"],
redacted_text=results["modified_prompt"],
risk_score=results["risk_score"],
)
def main():
"""
Main function to initialize and evaluate the secrets detectors.
"""
client = weave.init("parambharat/secrets-detection")
dataset = weave.ref("secrets-detection-benchmark:latest").get()
llm_guard_guardrail = LLMGuardSecretsDetector()
guardrails_ai_guardrail = GuardrailsAISecretsDetector()
guardrails_genie_guardrail = SecretsDetectionGuardrail()
all_guards = [
llm_guard_guardrail,
guardrails_ai_guardrail,
guardrails_genie_guardrail,
]
evaluation = weave.Evaluation(
dataset=dataset.rows,
scorers=[AccuracyMetric()],
)
for guard in all_guards:
name = guard.__class__.__name__
guardrail_manager = GuardrailManager(
guardrails=[
guard,
]
)
results = asyncio.run(
evaluation.evaluate(
guardrail_manager,
__weave={"display_name": f"{name}"},
)
)
print(results)
if __name__ == "__main__":
main()
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