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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Krish Dholakia 2024-03-13 12:28:19 -07:00 committed by GitHub
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# +------------------------------------+
#
# Prompt Injection Detection
#
# +------------------------------------+
# Thank you users! We ❤️ you! - Krrish & Ishaan
## Reject a call if it contains a prompt injection attack.
from typing import Optional, Literal
import litellm
from litellm.caching import DualCache
from litellm.proxy._types import UserAPIKeyAuth
from litellm.integrations.custom_logger import CustomLogger
from litellm._logging import verbose_proxy_logger
from litellm.utils import get_formatted_prompt
from fastapi import HTTPException
import json, traceback, re
from difflib import SequenceMatcher
from typing import List
class _ENTERPRISE_PromptInjectionDetection(CustomLogger):
# Class variables or attributes
def __init__(self):
self.verbs = [
"Ignore",
"Disregard",
"Skip",
"Forget",
"Neglect",
"Overlook",
"Omit",
"Bypass",
"Pay no attention to",
"Do not follow",
"Do not obey",
]
self.adjectives = [
"",
"prior",
"previous",
"preceding",
"above",
"foregoing",
"earlier",
"initial",
]
self.prepositions = [
"",
"and start over",
"and start anew",
"and begin afresh",
"and start from scratch",
]
def print_verbose(self, print_statement, level: Literal["INFO", "DEBUG"] = "DEBUG"):
if level == "INFO":
verbose_proxy_logger.info(print_statement)
elif level == "DEBUG":
verbose_proxy_logger.debug(print_statement)
if litellm.set_verbose is True:
print(print_statement) # noqa
def generate_injection_keywords(self) -> List[str]:
combinations = []
for verb in self.verbs:
for adj in self.adjectives:
for prep in self.prepositions:
phrase = " ".join(filter(None, [verb, adj, prep])).strip()
combinations.append(phrase.lower())
return combinations
def check_user_input_similarity(
self, user_input: str, similarity_threshold: float = 0.7
) -> bool:
user_input_lower = user_input.lower()
keywords = self.generate_injection_keywords()
for keyword in keywords:
# Calculate the length of the keyword to extract substrings of the same length from user input
keyword_length = len(keyword)
for i in range(len(user_input_lower) - keyword_length + 1):
# Extract a substring of the same length as the keyword
substring = user_input_lower[i : i + keyword_length]
# Calculate similarity
match_ratio = SequenceMatcher(None, substring, keyword).ratio()
if match_ratio > similarity_threshold:
self.print_verbose(
print_statement=f"Rejected user input - {user_input}. {match_ratio} similar to {keyword}",
level="INFO",
)
return True # Found a highly similar substring
return False # No substring crossed the threshold
async def async_pre_call_hook(
self,
user_api_key_dict: UserAPIKeyAuth,
cache: DualCache,
data: dict,
call_type: str, # "completion", "embeddings", "image_generation", "moderation"
):
try:
"""
- check if user id part of call
- check if user id part of blocked list
"""
self.print_verbose(f"Inside Prompt Injection Detection Pre-Call Hook")
try:
assert call_type in [
"completion",
"embeddings",
"image_generation",
"moderation",
"audio_transcription",
]
except Exception as e:
self.print_verbose(
f"Call Type - {call_type}, not in accepted list - ['completion','embeddings','image_generation','moderation','audio_transcription']"
)
return data
formatted_prompt = get_formatted_prompt(data=data, call_type=call_type) # type: ignore
is_prompt_attack = self.check_user_input_similarity(
user_input=formatted_prompt
)
if is_prompt_attack == True:
raise HTTPException(
status_code=400,
detail={
"error": "Rejected message. This is a prompt injection attack."
},
)
return data
except HTTPException as e:
raise e
except Exception as e:
traceback.print_exc()