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easyjailbreak/attacker/Cipher_Yuan_2023.py

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+"""
+Cipher Class
+============================================
+This Class enables humans to chat with LLMs through cipher prompts topped with 
+system role descriptions and few-shot enciphered demonstrations.
+
+Paper title：GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via Cipher
+arXiv Link: https://arxiv.org/pdf/2308.06463.pdf
+Source repository: https://github.com/RobustNLP/CipherChat
+"""
+import json
+import logging
+logging.basicConfig(level=logging.INFO)
+import pandas as pd
+from easyjailbreak.metrics.Evaluator import EvaluatorGenerativeJudge
+from easyjailbreak.attacker import AttackerBase
+from easyjailbreak.datasets import JailbreakDataset, Instance
+from easyjailbreak.mutation.rule import MorseExpert, CaesarExpert, AsciiExpert, SelfDefineCipher
+
+from tqdm import tqdm
+
+__all__ = ['Cipher']
+
+class Cipher(AttackerBase):
+    r"""
+    Cipher is a class for conducting jailbreak attacks on language models. It integrates attack
+    strategies and policies to evaluate and exploit weaknesses in target language models.
+    """
+    def __init__(self, attack_model, target_model, eval_model, jailbreak_datasets: JailbreakDataset, save_path, dataset_name):
+        super().__init__(attack_model, target_model, eval_model, jailbreak_datasets)
+        r"""
+        Initialize the Cipher Attacker.
+        :param attack_model: In this case, the attack_model should be set as None.
+        :param target_model: The target language model to be attacked.
+        :param eval_model: The evaluation model to evaluate the attack results.
+        :param jailbreak_datasets: The dataset to be attacked.
+        """
+        self.mutations = [
+            MorseExpert(),
+            CaesarExpert(),
+            AsciiExpert(),
+            SelfDefineCipher()
+            ]
+        self.evaluator = EvaluatorGenerativeJudge(eval_model)
+        self.info_dict = {'query': []}
+        self.info_dict.update({expert.__class__.__name__: [] for expert in self.mutations})
+        self.df = None
+
+        self.save_path = save_path
+        self.dataset_name = dataset_name
+
+    def single_attack(self, instance: Instance) -> JailbreakDataset:
+        r"""
+        Conduct four cipher attack_mehtods on a single source instance.
+        """
+        source_jailbreakdataset = JailbreakDataset([instance])
+        source_instance_list = []
+        updated_instance_list = []
+
+        for mutation in self.mutations:
+            transformed_JailbreakDatasets = mutation(source_jailbreakdataset)
+            for item in transformed_JailbreakDatasets:
+                source_instance_list.append(item)
+
+        for instance in source_instance_list:
+            answer = self.target_model.generate(instance.jailbreak_prompt.format(encoded_query = instance.encoded_query))
+            instance.encoded_target_responses = answer
+            updated_instance_list.append(instance)
+
+        for i,instance in enumerate(updated_instance_list):
+            mutation = self.mutations[i]
+            instance.target_responses.append(mutation.decode(instance.encoded_target_responses))
+            updated_instance_list[i] = instance
+
+        return JailbreakDataset(updated_instance_list)
+
+    def attack(self):
+        r"""
+        Execute the attack process using four cipher methods on the entire jailbreak_datasets.
+        """
+        logging.info("Jailbreak started!")
+        assert len(self.jailbreak_datasets) > 0, "The jailbreak_datasets must be a non-empty JailbreakDataset object."
+        self.attack_results = JailbreakDataset([])
+        try:
+            with open(self.save_path, 'w') as f:
+                for instance in tqdm(self.jailbreak_datasets):
+                    if self.dataset_name == 'trustllm':
+                        self.target_model.set_system_message(instance.system_message)
+
+                    self.info_dict['query'].append(instance.query)
+                    results = self.single_attack(instance)
+                    for new_instance in results:
+                        self.attack_results.add(new_instance)
+
+                        line = new_instance.to_dict()
+                        f.write(json.dumps(line, ensure_ascii=False) + '\n')
+        except KeyboardInterrupt:
+            logging.info("Jailbreak interrupted by user!")
+
+        # self.evaluator(self.attack_results)
+        # self.update(self.info_dict)
+        logging.info("Jailbreak finished!")
+
+    def update(self, dictionary: dict):
+        r"""
+        Update the state of the Cipher based on the evaluation results of attack_results.
+        """
+        keys_iterator = iter(list(dictionary.keys())[1:])
+        for evaluated_instance in self.attack_results:
+            try:
+                key = next(keys_iterator)
+                dictionary[key].append(evaluated_instance.eval_results[-1])
+            except StopIteration:
+                keys_iterator = iter(list(dictionary.keys())[1:])
+                key = next(keys_iterator)
+                dictionary[key].append(evaluated_instance.eval_results[-1])
+        self.df = pd.DataFrame(dictionary)
+        self.df['q_s_r'] = self.df.apply(lambda row: row[1:].sum() / len(row[1:]), axis=1)
+        column_probabilities = self.df.iloc[:, 1:].apply(lambda col: col.sum() / len(col))
+        column_probabilities = pd.Series(['m_s_r'] + list(column_probabilities), index=self.df.columns)
+        self.df.loc[self.df.index.max() + 1] = column_probabilities
+
+    def log(self):
+        r"""
+        Report the attack results.
+        """
+        logging.info("====================Jailbreak report:======================")
+        for column in self.df.columns[1:-1]:
+            logging.info(f"The success rate of {column}:{self.df[column].iloc[-1]* 100:.2f}%")
+        logging.info("================Success Rate for Each Item:===============")
+        for idx in self.df.index[:-1]:
+            query_string = self.df.loc[idx, self.df.columns[0]]
+            logging.info(f"{idx+1}.The jailbreak success rate of this query is {self.df.loc[idx].iloc[-1]* 100:.2f}%, {query_string}")
+        logging.info("==================Overall success rate:====================")
+        logging.info(f"{self.df.iloc[-1, -1]* 100:.2f}%")
+        logging.info("======================Report End============================")
+