app.py

import argparse
import glob
import json
import logging
import multiprocessing as mp
import os
import time
import uuid
from datetime import timedelta
from functools import lru_cache
from typing import List, Union

import aegis
import gradio as gr
import requests
from huggingface_hub import HfApi
from optimum.onnxruntime import ORTModelForSequenceClassification
from rebuff import Rebuff
from transformers import AutoTokenizer, pipeline

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

hf_api = HfApi(token=os.getenv("HF_TOKEN"))
num_processes = 2  # mp.cpu_count()

lakera_api_key = os.getenv("LAKERA_API_KEY")
automorphic_api_key = os.getenv("AUTOMORPHIC_API_KEY")
rebuff_api_key = os.getenv("REBUFF_API_KEY")


@lru_cache(maxsize=2)
def init_prompt_injection_model(prompt_injection_ort_model: str, subfolder: str = "") -> pipeline:
    hf_model = ORTModelForSequenceClassification.from_pretrained(
        prompt_injection_ort_model,
        export=False,
        subfolder=subfolder,
    )
    hf_tokenizer = AutoTokenizer.from_pretrained(prompt_injection_ort_model, subfolder=subfolder)
    hf_tokenizer.model_input_names = ["input_ids", "attention_mask"]

    logger.info(f"Initialized classification ONNX model {prompt_injection_ort_model} on CPU")

    return pipeline(
        "text-classification",
        model=hf_model,
        tokenizer=hf_tokenizer,
        device="cpu",
        batch_size=1,
        truncation=True,
        max_length=512,
    )


def convert_elapsed_time(diff_time) -> float:
    return round(timedelta(seconds=diff_time).total_seconds(), 2)


deepset_classifier = init_prompt_injection_model(
    "laiyer/deberta-v3-base-injection-onnx"
)  # ONNX version of deepset/deberta-v3-base-injection
laiyer_classifier = init_prompt_injection_model("laiyer/deberta-v3-base-prompt-injection", "onnx")
fmops_classifier = init_prompt_injection_model(
    "laiyer/fmops-distilbert-prompt-injection-onnx"
)  # ONNX version of fmops/distilbert-prompt-injection


def detect_hf(prompt: str, threshold: float = 0.5, classifier=laiyer_classifier) -> (bool, bool):
    try:
        pi_result = classifier(prompt)
        injection_score = round(
            pi_result[0]["score"]
            if pi_result[0]["label"] == "INJECTION"
            else 1 - pi_result[0]["score"],
            2,
        )

        logger.info(f"Prompt injection result from the HF model: {pi_result}")

        return True, injection_score > threshold
    except Exception as err:
        logger.error(f"Failed to call HF model: {err}")
        return False, False


def detect_hf_laiyer(prompt: str) -> (bool, bool):
    return detect_hf(prompt, classifier=laiyer_classifier)


def detect_hf_deepset(prompt: str) -> (bool, bool):
    return detect_hf(prompt, classifier=deepset_classifier)


def detect_hf_fmops(prompt: str) -> (bool, bool):
    return detect_hf(prompt, classifier=fmops_classifier)


def detect_lakera(prompt: str) -> (bool, bool):
    try:
        response = requests.post(
            "https://api.lakera.ai/v1/prompt_injection",
            json={"input": prompt},
            headers={"Authorization": f"Bearer {lakera_api_key}"},
        )
        response_json = response.json()
        logger.info(f"Prompt injection result from Lakera: {response.json()}")

        return True, response_json["results"][0]["flagged"]
    except requests.RequestException as err:
        logger.error(f"Failed to call Lakera API: {err}")
        return False, False


def detect_automorphic(prompt: str) -> (bool, bool):
    ag = aegis.Aegis(automorphic_api_key)
    try:
        ingress_attack_detected = ag.ingress(prompt, "")
        logger.info(f"Prompt injection result from Automorphic: {ingress_attack_detected}")
        return True, ingress_attack_detected["detected"]
    except Exception as err:
        logger.error(f"Failed to call Automorphic API: {err}")
        return False, False  # Assume it's not attack


def detect_rebuff(prompt: str) -> (bool, bool):
    try:
        rb = Rebuff(api_token=rebuff_api_key, api_url="https://www.rebuff.ai")
        result = rb.detect_injection(prompt)
        logger.info(f"Prompt injection result from Rebuff: {result}")

        return True, result.injectionDetected
    except Exception as err:
        logger.error(f"Failed to call Rebuff API: {err}")
        return False, False


detection_providers = {
    "Laiyer (HF model)": detect_hf_laiyer,
    "Deepset (HF model)": detect_hf_deepset,
    "FMOps (HF model)": detect_hf_fmops,
    "Lakera Guard": detect_lakera,
    "Automorphic Aegis": detect_automorphic,
    "Rebuff": detect_rebuff,
}


def is_detected(provider: str, prompt: str) -> (str, bool, bool, float):
    if provider not in detection_providers:
        logger.warning(f"Provider {provider} is not supported")
        return False, 0.0

    start_time = time.monotonic()
    request_result, is_injection = detection_providers[provider](prompt)
    end_time = time.monotonic()

    return provider, request_result, is_injection, convert_elapsed_time(end_time - start_time)


def execute(prompt: str, store_to_dataset: bool = True) -> List[Union[str, bool, float]]:
    results = []

    with mp.Pool(processes=num_processes) as pool:
        for result in pool.starmap(
            is_detected, [(provider, prompt) for provider in detection_providers.keys()]
        ):
            results.append(result)

    # Save image and result
    if store_to_dataset:
        fileobj = json.dumps({"prompt": prompt, "results": results}, indent=2).encode("utf-8")
        result_path = f"/prompts/train/{str(uuid.uuid4())}.json"
        hf_api.upload_file(
            path_or_fileobj=fileobj,
            path_in_repo=result_path,
            repo_id="laiyer/prompt-injection-benchmark",
            repo_type="dataset",
        )
        logger.info(f"Stored prompt: {prompt}")

    return results


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--port", type=int, default=7860)
    parser.add_argument("--url", type=str, default="0.0.0.0")
    args, left_argv = parser.parse_known_args()

    example_files = glob.glob(os.path.join(os.path.dirname(__file__), "examples", "*.txt"))
    examples = [open(file).read() for file in example_files]

    gr.Interface(
        fn=execute,
        inputs=[
            gr.Textbox(label="Prompt"),
            gr.Checkbox(
                label="Store prompt and results to the public dataset `laiyer/prompt-injection-benchmark`",
                value=True,
            ),
        ],
        outputs=[
            gr.Dataframe(
                headers=[
                    "Provider",
                    "Is request successful?",
                    "Is prompt injection?",
                    "Latency (seconds)",
                ],
                datatype=["str", "bool", "bool", "number"],
                label="Results",
            ),
        ],
        title="Prompt Injection Benchmark",
        description="This interface aims to benchmark the prompt injection detection providers. The results are stored in the public dataset for fairness of all sides.",
        examples=[
            [
                example,
                False,
            ]
            for example in examples
        ],
        cache_examples=True,
        allow_flagging="never",
    ).queue(1).launch(server_name=args.url, server_port=args.port)