From d82a3828f2664795e5605746ce9f04af4ddcdc7b Mon Sep 17 00:00:00 2001 From: Oleksandr Yaremchuk Date: Tue, 19 Dec 2023 02:50:21 +0100 Subject: [PATCH] Improve prompt injection detection (#14842) - **Description:** This is addition to [my previous PR](https://github.com/langchain-ai/langchain/pull/13930) with improvements to flexibility allowing different models and notebook to use ONNX runtime for faster speed. Since the last PR, [our model](https://huggingface.co/laiyer/deberta-v3-base-prompt-injection) got more than 660k downloads, and with the [public benchmark](https://huggingface.co/spaces/laiyer/prompt-injection-benchmark) showed much fewer false-positives than the previous one from deepset. Additionally, on the ONNX runtime, it can be running 3x faster on the CPU, which might be handy for builders using Langchain. **Issue:** N/A - **Dependencies:** N/A - **Tag maintainer:** N/A - **Twitter handle:** `@laiyer_ai` --- .../hugging_face_prompt_injection.ipynb | 124 ++++++++++++------ .../hugging_face_identifier.py | 58 ++++++-- 2 files changed, 131 insertions(+), 51 deletions(-) diff --git a/docs/docs/guides/safety/hugging_face_prompt_injection.ipynb b/docs/docs/guides/safety/hugging_face_prompt_injection.ipynb index 21224ea0b0..040f7ff242 100644 --- a/docs/docs/guides/safety/hugging_face_prompt_injection.ipynb +++ b/docs/docs/guides/safety/hugging_face_prompt_injection.ipynb @@ -8,7 +8,10 @@ "# Hugging Face prompt injection identification\n", "\n", "This notebook shows how to prevent prompt injection attacks using the text classification model from `HuggingFace`.\n", - "By default it uses a *deberta* model trained to identify prompt injections. In this walkthrough we'll use https://huggingface.co/laiyer/deberta-v3-base-prompt-injection." + "\n", + "By default, it uses a *[laiyer/deberta-v3-base-prompt-injection](https://huggingface.co/laiyer/deberta-v3-base-prompt-injection)* model trained to identify prompt injections. \n", + "\n", + "In this notebook, we will use the ONNX version of the model to speed up the inference. " ] }, { @@ -16,42 +19,72 @@ "id": "83cbecf2-7d0f-4a90-9739-cc8192a35ac3", "metadata": {}, "source": [ - "## Usage" + "## Usage\n", + "\n", + "First, we need to install the `optimum` library that is used to run the ONNX models:" ] }, { "cell_type": "code", "execution_count": null, + "id": "9bdbfdc7c949a9c1", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "!pip install \"optimum[onnxruntime]\"" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "fcdd707140e8aba1", + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-18T11:41:24.738278Z", + "start_time": "2023-12-18T11:41:20.842567Z" + }, + "collapsed": false + }, + "outputs": [], + "source": [ + "from transformers import pipeline, AutoTokenizer\n", + "from optimum.onnxruntime import ORTModelForSequenceClassification\n", + "\n", + "# Using https://huggingface.co/laiyer/deberta-v3-base-prompt-injection\n", + "model_path = \"laiyer/deberta-v3-base-prompt-injection\"\n", + "tokenizer = AutoTokenizer.from_pretrained(model_path)\n", + "tokenizer.model_input_names = [\"input_ids\", \"attention_mask\"] # Hack to run the model\n", + "model = ORTModelForSequenceClassification.from_pretrained(model_path, subfolder=\"onnx\")\n", + "\n", + "classifier = pipeline(\n", + " \"text-classification\",\n", + " model=model,\n", + " tokenizer=tokenizer,\n", + " truncation=True,\n", + " max_length=512,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, "id": "aea25588-3c3f-4506-9094-221b3a0d519b", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-18T11:41:24.747720Z", + "start_time": "2023-12-18T11:41:24.737587Z" + } + }, "outputs": [ { "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "58ab3557623a495d8cc3c3e32a61938f", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Downloading config.json: 0%| | 0.00/994 [00:00 1\u001b[0m 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reverse\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 53\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mINJECTION\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m---> 54\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPrompt injection attack detected\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 55\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m query\n", "\u001b[0;31mValueError\u001b[0m: Prompt injection attack detected" ] } @@ -320,9 +360,9 @@ ], "metadata": { "kernelspec": { - "display_name": "poetry-venv", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "poetry-venv" + "name": "python3" }, "language_info": { "codemirror_mode": { diff --git a/libs/experimental/langchain_experimental/prompt_injection_identifier/hugging_face_identifier.py b/libs/experimental/langchain_experimental/prompt_injection_identifier/hugging_face_identifier.py index 22412a5896..949346ca36 100644 --- a/libs/experimental/langchain_experimental/prompt_injection_identifier/hugging_face_identifier.py +++ b/libs/experimental/langchain_experimental/prompt_injection_identifier/hugging_face_identifier.py @@ -1,7 +1,7 @@ """Tool for the identification of prompt injection attacks.""" from __future__ import annotations -from typing import TYPE_CHECKING, Any +from typing import TYPE_CHECKING, Union from langchain.pydantic_v1 import Field, root_validator from langchain.tools.base import BaseTool @@ -10,17 +10,39 @@ if TYPE_CHECKING: from transformers import Pipeline +class PromptInjectionException(ValueError): + def __init__(self, message="Prompt injection attack detected", score: float = 1.0): + self.message = message + self.score = score + + super().__init__(self.message) + + def _model_default_factory( - model_name: str = "deepset/deberta-v3-base-injection" + model_name: str = "laiyer/deberta-v3-base-prompt-injection", ) -> Pipeline: try: - from transformers import pipeline + from transformers import ( + AutoModelForSequenceClassification, + AutoTokenizer, + pipeline, + ) except ImportError as e: raise ImportError( "Cannot import transformers, please install with " "`pip install transformers`." ) from e - return pipeline("text-classification", model=model_name) + + tokenizer = AutoTokenizer.from_pretrained(model_name) + model = AutoModelForSequenceClassification.from_pretrained(model_name) + + return pipeline( + "text-classification", + model=model, + tokenizer=tokenizer, + max_length=512, # default length of BERT models + truncation=True, # otherwise it will fail on long prompts + ) class HuggingFaceInjectionIdentifier(BaseTool): @@ -32,13 +54,26 @@ class HuggingFaceInjectionIdentifier(BaseTool): "Useful for when you need to ensure that prompt is free of injection attacks. " "Input should be any message from the user." ) - model: Any = Field(default_factory=_model_default_factory) + model: Union[Pipeline, str, None] = Field(default_factory=_model_default_factory) """Model to use for prompt injection detection. Can be specified as transformers Pipeline or string. String should correspond to the model name of a text-classification transformers model. Defaults to - ``deepset/deberta-v3-base-injection`` model. + ``laiyer/deberta-v3-base-prompt-injection`` model. """ + threshold: float = Field( + description="Threshold for prompt injection detection.", default=0.5 + ) + """Threshold for prompt injection detection. + + Defaults to 0.5.""" + injection_label: str = Field( + description="Label of the injection for prompt injection detection.", + default="INJECTION", + ) + """Label for prompt injection detection model. + + Defaults to ``INJECTION``. Value depends on the model used.""" @root_validator(pre=True) def validate_environment(cls, values: dict) -> dict: @@ -49,7 +84,12 @@ class HuggingFaceInjectionIdentifier(BaseTool): def _run(self, query: str) -> str: """Use the tool.""" result = self.model(query) - result = sorted(result, key=lambda x: x["score"], reverse=True) - if result[0]["label"] == "INJECTION": - raise ValueError("Prompt injection attack detected") + score = ( + result[0]["score"] + if result[0]["label"] == self.injection_label + else 1 - result[0]["score"] + ) + if score > self.threshold: + raise PromptInjectionException("Prompt injection attack detected", score) + return query