forked from phoenix/litellm-mirror
Merge pull request #2498 from BerriAI/litellm_prompt_injection_detection
feat(prompt_injection_detection.py): support simple heuristic similarity check for prompt injection attacks
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3 changed files with 190 additions and 0 deletions
144
enterprise/enterprise_hooks/prompt_injection_detection.py
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144
enterprise/enterprise_hooks/prompt_injection_detection.py
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# +------------------------------------+
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#
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# Prompt Injection Detection
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#
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# +------------------------------------+
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# Thank you users! We ❤️ you! - Krrish & Ishaan
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## Reject a call if it contains a prompt injection attack.
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from typing import Optional, Literal
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import litellm
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from litellm.caching import DualCache
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from litellm.proxy._types import UserAPIKeyAuth
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from litellm.integrations.custom_logger import CustomLogger
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from litellm._logging import verbose_proxy_logger
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from litellm.utils import get_formatted_prompt
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from fastapi import HTTPException
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import json, traceback, re
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from difflib import SequenceMatcher
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from typing import List
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class _ENTERPRISE_PromptInjectionDetection(CustomLogger):
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# Class variables or attributes
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def __init__(self):
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self.verbs = [
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"Ignore",
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"Disregard",
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"Skip",
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"Forget",
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"Neglect",
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"Overlook",
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"Omit",
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"Bypass",
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"Pay no attention to",
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"Do not follow",
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"Do not obey",
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]
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self.adjectives = [
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"",
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"prior",
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"previous",
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"preceding",
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"above",
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"foregoing",
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"earlier",
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"initial",
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]
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self.prepositions = [
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"",
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"and start over",
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"and start anew",
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"and begin afresh",
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"and start from scratch",
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]
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def print_verbose(self, print_statement, level: Literal["INFO", "DEBUG"] = "DEBUG"):
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if level == "INFO":
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verbose_proxy_logger.info(print_statement)
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elif level == "DEBUG":
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verbose_proxy_logger.debug(print_statement)
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if litellm.set_verbose is True:
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print(print_statement) # noqa
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def generate_injection_keywords(self) -> List[str]:
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combinations = []
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for verb in self.verbs:
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for adj in self.adjectives:
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for prep in self.prepositions:
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phrase = " ".join(filter(None, [verb, adj, prep])).strip()
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combinations.append(phrase.lower())
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return combinations
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def check_user_input_similarity(
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self, user_input: str, similarity_threshold: float = 0.7
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) -> bool:
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user_input_lower = user_input.lower()
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keywords = self.generate_injection_keywords()
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for keyword in keywords:
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# Calculate the length of the keyword to extract substrings of the same length from user input
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keyword_length = len(keyword)
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for i in range(len(user_input_lower) - keyword_length + 1):
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# Extract a substring of the same length as the keyword
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substring = user_input_lower[i : i + keyword_length]
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# Calculate similarity
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match_ratio = SequenceMatcher(None, substring, keyword).ratio()
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if match_ratio > similarity_threshold:
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self.print_verbose(
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print_statement=f"Rejected user input - {user_input}. {match_ratio} similar to {keyword}",
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level="INFO",
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)
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return True # Found a highly similar substring
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return False # No substring crossed the threshold
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async def async_pre_call_hook(
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self,
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user_api_key_dict: UserAPIKeyAuth,
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cache: DualCache,
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data: dict,
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call_type: str, # "completion", "embeddings", "image_generation", "moderation"
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):
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try:
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"""
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- check if user id part of call
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- check if user id part of blocked list
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"""
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self.print_verbose(f"Inside Prompt Injection Detection Pre-Call Hook")
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try:
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assert call_type in [
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"completion",
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"embeddings",
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"image_generation",
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"moderation",
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"audio_transcription",
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]
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except Exception as e:
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self.print_verbose(
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f"Call Type - {call_type}, not in accepted list - ['completion','embeddings','image_generation','moderation','audio_transcription']"
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)
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return data
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formatted_prompt = get_formatted_prompt(data=data, call_type=call_type) # type: ignore
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is_prompt_attack = self.check_user_input_similarity(
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user_input=formatted_prompt
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)
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if is_prompt_attack == True:
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raise HTTPException(
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status_code=400,
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detail={
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"error": "Rejected message. This is a prompt injection attack."
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},
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)
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return data
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except HTTPException as e:
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raise e
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except Exception as e:
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traceback.print_exc()
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