attack.py

"""main hook to start the llm-confidentiality framework"""
# -*- coding: utf-8 -*-
# !/usr/bin/env python3

import os
import subprocess
import psutil
import getpass
from pathlib import Path
import sys
import time
import datetime
import argparse
import shutil
from typing import List

import openai
import torch
from huggingface_hub import login

from framework.strategy import SecretKeyAttackStrategy, LangchainAttackStrategy
from framework.attacks import ATTACK_LIST, match_attack
from framework.defenses import DEFENSES_LIST, match_defense
from framework.colors import TColors
from framework.utils import log_results
from framework.scenarios import Scenarios


if not os.path.isdir("/mnt/NVME_A/"):
    if not os.path.isdir(str(Path.home() / "data")):
        os.mkdir(str(Path.home() / "data"))
    os.environ["HF_HOME"] = str(Path.home() / "data")
else:
    os.environ["HF_HOME"] = "/mnt/NVME_A/"


def main(
    attacks: List[str],
    defenses: List[str],
    llm_type: str,
    llm_guessing: bool,
    temperature: float,
    iterations: int,
    create_prompt_dataset: bool,
    create_response_dataset: bool,
    name_suffix: str,
    strategy: str,
    scenario: str,
    verbose: bool,
    device: str,
    prompt_format: str,
    disable_safeguards: bool,
) -> None:
    """
    Main function to start the llm-confidentiality testing procedures.

    Parameters:
        attack: List[str] - specifies a list of attacks against the LLM
        defenses: List[str] - specifies the defense type
        llm_type: str - specifies the opponent LLM type
        llm_guessing: bool - specifies whether to use the LLM eval to guess the secret or not
        temperature: float - specifies the opponent LLM temperature to control randomness
        iterations: int - number of attack iterations to test system prompts against
        create_prompt_dataset: bool - specifies whether to create a system prompt dataset or not
        create_response_dataset: bool - specifies whether to create a responses dataset or not
        name_suffix: str - adds a name suffix for loading custom models
        strategy: str - specifies the attack strategy to use (secretkey or langchain)
        scenario: str - specifies the scenario to use for langchain attacks
        verbose: bool - enables a more verbose logging output
        prompt_format: str - specifies the format of the llms prompt (react or tool-finetuned)
        disable_safeguards: bool - disables system prompt safeguards

    Returns:
        None
    """
    start = time.perf_counter()  # start timer
    subprocess.Popen(
        "ollama serve",
        shell=True,
    )

    # paste the OpenAI key into the key.txt file and put into the root directory
    try:
        with open(file="key.txt", mode="r", encoding="utf-8") as f:
            key = f.read().replace("\n", "")
            assert key != "", f"{TColors.FAIL}Key is empty.{TColors.ENDC}"

            os.environ["OPENAI_API_KEY"] = key
            openai.api_key = key
            print(f"{TColors.OKGREEN}OpenAI API key loaded.{TColors.ENDC}")

    except FileNotFoundError:
        print(
            f"{TColors.FAIL}Please paste your OpenAI API key into the key.txt "
            f"file and put it into the root directory.{TColors.ENDC}"
        )
        if llm_type in ["gpt-3.5-turbo", "gpt-3.5-turbo-0301", "gpt-4"]:
            sys.exit(1)

    # paste the Huggingface token into the hf_token.txt file and put into the root directory
    try:
        with open(file="hf_token.txt", mode="r", encoding="utf-8") as f:
            key = f.read().replace("\n", "")
            assert key != "", f"{TColors.FAIL}HF Token is empty.{TColors.ENDC}"

            os.environ["HF_TOKEN"] = key
            print(f"{TColors.OKGREEN}Huggingface token loaded.")
            login(token=key, add_to_git_credential=True)
            print(f"{TColors.ENDC}")

    except FileNotFoundError:
        print(
            f"{TColors.FAIL}Please paste your Huggingface token into the hf_token.txt "
            f"file and put it into the root directory.{TColors.ENDC}"
        )
        if llm_type in ["llama2", "llama2-7b", "llama2-13b", "llama2-70b"]:
            sys.exit(1)

    # set the devices correctly
    if device == "cpu":
        device = torch.device("cpu")
    elif device == "cuda" and torch.cuda.is_available():
        device = torch.device(device)
    elif device == "mps" and torch.backends.mps.is_available():
        device = torch.device(device)
    else:
        print(
            f"{TColors.WARNING}Warning{TColors.ENDC}: Device {TColors.OKCYAN}{device} "
            f"{TColors.ENDC}is not available. Setting device to CPU instead."
        )
        device = torch.device("cpu")

    if "all" in attacks:
        attacks = ATTACK_LIST
        if "llama3" in llm_type:
            # llama 3 models do not work with typoglycemia and obfuscation attacks
            attacks.pop(attacks.index("obfuscation"))
            attacks.pop(attacks.index("typoglycemia"))

    if "all" in defenses:
        defenses = DEFENSES_LIST

    # set the scenario string properly
    scenario = [s.lower() for s in scenario]
    scenario_print = []
    scenario_list = []

    if "all" in scenario:
        scenario_print = list(Scenarios.__members__.keys())
        scenario_list = list(Scenarios)

    else:
        for scenario_iter in Scenarios:
            if scenario_iter.name.lower() in scenario:
                scenario_print.append(scenario_iter.name)
                scenario_list.append(scenario_iter)

    # add '-' in front of the name suffix
    if name_suffix != "" and not name_suffix.startswith("-"):
        name_suffix = "-" + name_suffix

    # if iterations are less than number of attacks, set the iterations to the number of attacks
    if iterations < len(attacks):
        iterations = len(attacks)
        print(
            f"{TColors.WARNING}Warning{TColors.ENDC}: Iterations were less then number of "
            f"Attacks. Set number of iterations to {len(attacks)}."
        )

    if (
        prompt_format == "tool-finetuned"
        and not llm_type.startswith("llama3")
        and not llm_type.startswith("gpt")
        and not llm_type.startswith("reflection")
        and not llm_type.startswith("qwen2.5")
        and "deepseek" not in llm_type
    ):
        print(
            f"{TColors.WARNING}Warning{TColors.ENDC}: Tool finetuned format is only available "
            f"for LLama, Deepseek, and GPT models. Setting prompt_format to react instead."
        )
        prompt_format = "react"

    print(
        "\n"
        + f"## {TColors.BOLD}{TColors.HEADER}{TColors.UNDERLINE}System Information"
        + f"{TColors.ENDC} "
        + "#" * (shutil.get_terminal_size().columns - 23)
    )
    print(
        f"## {TColors.OKBLUE}{TColors.BOLD}Date{TColors.ENDC}: "
        + str(datetime.datetime.now().strftime("%A, %d. %B %Y %I:%M%p"))
    )
    print(
        f"## {TColors.OKBLUE}{TColors.BOLD}System{TColors.ENDC}: "
        f"{torch.get_num_threads()} CPU cores with {os.cpu_count()} threads and "
        f"{torch.cuda.device_count()} GPUs on user: {getpass.getuser()}"
    )
    print(f"## {TColors.OKBLUE}{TColors.BOLD}Device{TColors.ENDC}: {device}")
    if (device == "cuda" or torch.device("cuda")) and torch.cuda.is_available():
        print(
            f"## {TColors.OKBLUE}{TColors.BOLD}GPU Memory{TColors.ENDC}: "
            f"{torch.cuda.mem_get_info()[1] // 1024**2} MB"
        )
    elif (device == "mps" or torch.device("mps")) and torch.backends.mps.is_available():
        print(
            f"## {TColors.OKBLUE}{TColors.BOLD}Shared Memory{TColors.ENDC}: "
            f"{psutil.virtual_memory()[0] // 1024**2} MB"
        )
    else:
        print(
            f"## {TColors.OKBLUE}{TColors.BOLD}CPU Memory{TColors.ENDC}: "
            f"{psutil.virtual_memory()[0] // 1024**2} MB"
        )
    print(
        f"## {TColors.BOLD}{TColors.HEADER}{TColors.UNDERLINE}Parameters"
        + f"{TColors.ENDC} "
        + "#" * (shutil.get_terminal_size().columns - 14)
    )
    print(f"## {TColors.OKBLUE}{TColors.BOLD}Attack Type{TColors.ENDC}: {attacks}")
    print(f"## {TColors.OKBLUE}{TColors.BOLD}Defense Type{TColors.ENDC}: {defenses}")
    print(
        f"## {TColors.OKBLUE}{TColors.BOLD}Opponent LLM{TColors.ENDC}: "
        f"{TColors.HEADER}{llm_type}{TColors.OKCYAN}{name_suffix}{TColors.ENDC}"
    )
    print(
        f"## {TColors.OKBLUE}{TColors.BOLD}Total Iterations{TColors.ENDC}: {iterations}"
    )
    print(f"## {TColors.OKBLUE}{TColors.BOLD}Temperature{TColors.ENDC}: {temperature}")
    print(
        f"## {TColors.OKBLUE}{TColors.BOLD}LLM Guessing{TColors.ENDC}: {llm_guessing}"
    )
    if strategy in ["tools", "langchain", "LangChain", "lang_chain", "lang-chain"]:
        print(
            f"## {TColors.OKBLUE}{TColors.BOLD}Attack Strategy{TColors.ENDC}: {strategy}"
        )
        print(
            f"## {TColors.OKBLUE}{TColors.BOLD}Scenario(s){TColors.ENDC}: {scenario_print}"
        )
    else:
        print(
            f"## {TColors.OKBLUE}{TColors.BOLD}Strategy{TColors.ENDC}: normal secrey-key game"
        )
    print(
        f"## {TColors.OKBLUE}{TColors.BOLD}Format{TColors.ENDC}: "
        f"{TColors.HEADER}{prompt_format}{TColors.ENDC}"
    )
    if disable_safeguards:
        print(
            f"## {TColors.OKBLUE}{TColors.BOLD}System Prompt Safeguards{TColors.ENDC}: "
            f"{TColors.FAIL}disabled{TColors.ENDC}"
        )
    else:
        print(
            f"## {TColors.OKBLUE}{TColors.BOLD}System Prompt Safeguards{TColors.ENDC}: "
            f"{TColors.OKGREEN}enabled{TColors.ENDC}"
        )
    if verbose:
        print(
            f"## {TColors.OKBLUE}{TColors.BOLD}Verbose Logging{TColors.ENDC}: {verbose}"
        )
    if create_prompt_dataset:
        print(
            f"## {TColors.OKBLUE}{TColors.BOLD}Creating System Prompt Dataset{TColors.ENDC}: "
            f"{create_prompt_dataset}"
        )
    if create_response_dataset:
        print(
            f"## {TColors.OKBLUE}{TColors.BOLD}Creating Responses Dataset{TColors.ENDC}: "
            f"{create_response_dataset}"
        )
    print("#" * shutil.get_terminal_size().columns + "\n")

    total_successes: dict[int] = {f"{attack}": 0 for attack in attacks}
    total_errors: dict[int] = {f"{attack}": 0 for attack in attacks}
    # divide the iterations by the number of attacks so every attack gets the same amount
    iterations = iterations // len(attacks)

    # initialize the strategy
    overwrite_chat = True
    overwrite_results = True
    if strategy in ["tools", "langchain", "LangChain", "lang_chain", "lang-chain"]:
        for exec_scenario in scenario_list:
            print(
                f"{TColors.HEADER}{TColors.BOLD}>> Executing Scenario: "
                f"{exec_scenario.name}{TColors.ENDC}"
            )

            # initialize the attack strategy
            attack_strategy = LangchainAttackStrategy(
                attack_func=match_attack(attacks[0]),
                defense_func=match_defense(defenses[0]),
                llm_type=llm_type,
                llm_suffix=name_suffix,
                llm_guessing=llm_guessing,
                temperature=temperature,
                iterations=iterations,
                create_prompt_dataset=create_prompt_dataset,
                create_response_dataset=create_response_dataset,
                scenario=exec_scenario,
                verbose=verbose,
                device=device,
                prompt_format=prompt_format,
                disable_safeguards=disable_safeguards,
            )

            for defense in defenses:
                # set the defense function
                defense_func = match_defense(defense)

                for attack in attacks:
                    # set the attack function
                    attack_func = match_attack(attack)

                    # set the attack and defense functions
                    attack_strategy.set_attack_func(attack_func)
                    attack_strategy.set_defense_func(defense_func)
                    # run the attack
                    total_successes[attack], total_errors[attack] = (
                        attack_strategy.execute(overwrite=overwrite_chat)
                    )
                    torch.cuda.empty_cache()
                    overwrite_chat = (
                        False  # set to false to save this strategy run completetly
                    )

                # print and log the results
                sum_successes = sum(total_successes.values())
                sum_iterations_without_errors = iterations * len(attacks) - sum(
                    total_errors.values()
                )
                if sum_iterations_without_errors == 0:
                    avg_succ = 0
                else:
                    avg_succ = round(
                        sum_successes / sum_iterations_without_errors * 100, 2
                    )

                print(f"{TColors.OKBLUE}{TColors.BOLD}>> Attack Results:{TColors.ENDC}")
                for attack, successes in total_successes.items():
                    print(
                        f"Attack: {TColors.OKCYAN}{attack}{TColors.ENDC} - Successes: {successes}"
                        f"/{iterations} ({total_errors[attack]} errors)"
                    )
                print(
                    f"{TColors.OKCYAN}{TColors.BOLD}>> Successrate:{TColors.ENDC} "
                    f"{TColors.BOLD}{TColors.HEADER}{avg_succ}{TColors.ENDC}"
                )

                log_results(
                    llm_name=llm_type + name_suffix,
                    defense_name=defense,
                    success_dict=total_successes,
                    error_dict=total_errors,
                    iters=iterations,
                    overwrite=overwrite_results,
                    scenario=exec_scenario.name,
                )
                overwrite_results = (
                    False  # set to false to save this strategy run completetly
                )

    else:
        attack_strategy = SecretKeyAttackStrategy(
            attack_func=None,
            defense_func=None,
            llm_type=llm_type,
            llm_suffix=name_suffix,
            llm_guessing=llm_guessing,
            temperature=temperature,
            iterations=iterations,
            create_prompt_dataset=create_prompt_dataset,
            create_response_dataset=create_response_dataset,
            verbose=verbose,
            device=device,
            prompt_format=prompt_format,
        )

        for defense in defenses:
            # set the defense function
            defense_func = match_defense(defense)

            for attack in attacks:
                # set the attack function
                attack_func = match_attack(attack)

                # set the attack and defense functions
                attack_strategy.set_attack_func(attack_func)
                attack_strategy.set_defense_func(defense_func)
                # run the attack
                total_successes[attack], total_errors[attack] = (
                    attack_strategy.execute()
                )
                torch.cuda.empty_cache()

            # print and log the results
            sum_successes = sum(total_successes.values())
            sum_iterations_without_errors = iterations * len(attacks) - sum(
                total_errors.values()
            )
            if sum_iterations_without_errors == 0:
                avg_succ = 0
            else:
                avg_succ = round(sum_successes / sum_iterations_without_errors * 100, 2)

            print(f"{TColors.OKBLUE}{TColors.BOLD}>> Attack Results:{TColors.ENDC}")
            for attack, successes in total_successes.items():
                print(
                    f"Attack: {TColors.OKCYAN}{attack}{TColors.ENDC} - Successes: {successes}/"
                    f"{iterations} ({total_errors[attack]} errors)"
                )
            print(
                f"{TColors.OKCYAN}{TColors.BOLD}>> Successrate:{TColors.ENDC} "
                f"{TColors.BOLD}{TColors.HEADER}{avg_succ}{TColors.ENDC}"
            )

            log_results(
                llm_name=llm_type + name_suffix,
                defense_name=defense,
                success_dict=total_successes,
                error_dict=total_errors,
                iters=iterations,
            )

    end = time.perf_counter()
    duration = (round(end - start) / 60.0) / 60.0
    print(f"{TColors.HEADER}Computation Time: {duration}{TColors.ENDC}")

    return 0


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="llm-confidentiality")
    parser.add_argument(
        "--attacks",
        "-a",
        type=str,
        default=["payload_splitting"],
        help="specifies the attack types",
        nargs="+",
    )
    parser.add_argument(
        "--defenses",
        "-d",
        type=str,
        default=["none"],
        help="specifies the defense type",
        nargs="+",
    )
    parser.add_argument(
        "--llm_type",
        "-llm",
        type=str,
        default="llama3-8b",
        help="specifies the opponent LLM type",
    )
    parser.add_argument(
        "--llm_guessing",
        "-lg",
        help="uses a second LLM to guess the secret",
        action="store_true",
        default=False,
    )
    parser.add_argument(
        "--temperature",
        "-t",
        type=float,
        default=0.0,
        help="specifies the opponent LLM temperature",
    )
    parser.add_argument(
        "--iterations",
        "-i",
        type=int,
        default=100,
        help="specifies the number of iterations to test systems prompts",
    )
    parser.add_argument(
        "--create_prompt_dataset",
        "-cp",
        help="enabl. sys prompt dataset creation",
        action="store_true",
        default=False,
    )
    parser.add_argument(
        "--create_response_dataset",
        "-cr",
        help="enabl. response dataset creation",
        action="store_true",
        default=False,
    )
    parser.add_argument(
        "--name_suffix",
        "-n",
        help="adds a name suffix for loading custom models",
        default="",
        type=str,
    )
    parser.add_argument(
        "--strategy",
        "-s",
        help="which strategy to use (secretkey or langchain)",
        default="",
        type=str,
    )
    parser.add_argument(
        "--scenario",
        "-sc",
        help="which scenario to use for tool based attacks",
        default=["all"],
        type=str,
        nargs="+",
    )
    parser.add_argument(
        "--verbose",
        "-v",
        help="enables a more verbose logging output",
        action="store_true",
        default=False,
    )
    parser.add_argument(
        "--device",
        "-dx",
        type=str,
        default="cpu",
        help="specifies the device to run the computations on (cpu, cuda, mps)",
    )
    parser.add_argument(
        "--prompt_format",
        "-pf",
        type=str,
        default="react",
        help="specifies the format of the llms prompt (react or tool-finetuned)",
    )
    parser.add_argument(
        "--disable_safeguards",
        "-ds",
        help="disables system prompt safeguards",
        action="store_true",
        default=False,
    )
    args = parser.parse_args()
    main(**vars(args))