01bb07dba8
FocalLoRA, Should-It-Be-Executed-Or-Processed, and topicattack were nested git repos (with an inner FocalLoRA/data/FocalLoRA/.git as well). Drop their .git history and track the contents directly in this repo instead of as submodules/gitlinks. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
74 lines
3.2 KiB
Python
74 lines
3.2 KiB
Python
import argparse
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import copy
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import tqdm
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import torch
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torch.backends.cudnn.deterministic = True
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from utils import jload, Logger, set_seeds
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from chatbot import HuggingfaceChatbot, GPTChatbot, OpensourceAPIChatbot
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from shield import BaselineProcessor
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from attack_defense_tools import *
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def load_model(model_path, system_path):
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if "gpt" in model_path.lower():
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model = GPTChatbot(model_path, system_path)
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elif any([t in model_path.lower() for t in ["70b","72b","405b","27b"]]):
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model = OpensourceAPIChatbot(model_path, system_path)
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else:
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model = HuggingfaceChatbot(model_path, system_path)
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return model
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def run_defense(args):
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logger = Logger(args.log_file)
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logger.log(str(args))
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# data = jload(args.data_path)
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victim_model = load_model(args.victim_model_path, args.victim_system_path)
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attack_model = None
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if args.attack_model_path is not None:
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attack_model = load_model(args.attack_model_path, args.attack_system_path)
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shield_processor = BaselineProcessor(args.input_template_path)
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for attack in args.attacks:
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for defense in args.defenses:
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acc_items = []
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asr_items = []
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data = jload(args.data_path)
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# random.shuffle(data)
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for d_item in tqdm.tqdm(data):
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output = d_item["output"]
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injection_output = d_item["injection_output"]
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user_input = shield_processor.construct_input(copy.deepcopy(d_item), eval(attack), eval(defense), model=attack_model, side=args.side)
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response = victim_model.respond(user_input)
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logger.log("*****************")
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logger.log(user_input)
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logger.log(response)
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acc_items.append(output.lower() in response.lower())
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asr_items.append(injection_output.lower() in response.lower())
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acc = sum(acc_items) / len(acc_items) * 100
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asr = sum(asr_items) / len(asr_items) * 100
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logger.log(f"############ attack:{attack}, defense:{defense} ############")
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logger.log(f"Accuracy: {acc:.2f}%")
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logger.log(f"ASR: {asr:.2f}%")
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logger.log(f"##################################")
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument("--victim_model_path", type=str, default="gpt-4o")
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parser.add_argument("--attack_model_path", type=str, default=None)
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parser.add_argument("--data_path", type=str, default='data/crafted_instruction_data_squad_injection_qa.json')
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parser.add_argument("--victim_system_path", type=str, default="prompts/generator_system_prompt.txt")
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parser.add_argument("--attack_system_path", type=str, default="prompts/generator_system_prompt.txt")
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parser.add_argument("--input_template_path", type=str, default="prompts/victim_instruction_data_template.txt")
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parser.add_argument("--attacks", type=str, nargs='+', default=["naive"])
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parser.add_argument("--defenses", type=str, nargs='+', default=["none"])
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parser.add_argument("--seed", type=int, default=42)
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parser.add_argument("--side", type=str, default="end")
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parser.add_argument("--log_file", type=str, default="logs/log.txt")
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args = parser.parse_args()
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set_seeds(args)
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run_defense(args) |