import dataclasses import os import re import time from datetime import datetime from typing import Any, Dict, List, Tuple import loguru from langfuse.model import InitialGeneration, Usage from pentestgpt.utils.llm_api import LLMAPI from openai import OpenAI import openai logger = loguru.logger logger.remove() # logger.add(level="WARNING", sink="logs/deepseek.log") @dataclasses.dataclass class Message: ask_id: str = None ask: dict = None answer: dict = None answer_id: str = None request_start_timestamp: float = None request_end_timestamp: float = None time_escaped: float = None @dataclasses.dataclass class Conversation: conversation_id: str = None message_list: List[Message] = dataclasses.field(default_factory=list) def __hash__(self): return hash(self.conversation_id) def __eq__(self, other): if not isinstance(other, Conversation): return False return self.conversation_id == other.conversation_id class DeepseekAPI(LLMAPI): def __init__(self, config_class, use_langfuse_logging=False): self.name = str(config_class.model) api_key = os.getenv("DEEPSEEK_API_KEY", None) self.client = OpenAI(api_key=api_key, base_url=config_class.api_base) if use_langfuse_logging: # use langfuse.openai to shadow the default openai library os.environ["LANGFUSE_PUBLIC_KEY"] = ( "pk-lf-5655b061-3724-43ee-87bb-28fab0b5f676" # do not modify ) os.environ["LANGFUSE_SECRET_KEY"] = ( "sk-lf-c24b40ef-8157-44af-a840-6bae2c9358b0" # do not modify ) from langfuse import Langfuse self.langfuse = Langfuse() self.model = config_class.model self.log_dir = config_class.log_dir self.history_length = 5 # maintain 5 messages in the history. (5 chat memory) self.conversation_dict: Dict[str, Conversation] = {} self.error_wait_time = config_class.error_wait_time logger.add(sink=os.path.join(self.log_dir, "chatgpt.log"), level="WARNING") def _chat_completion(self, history: List, model=None, image_url: str = None) -> str: generationStartTime = datetime.now() # use model if provided, otherwise use self.model; if self.model is None, use gpt-4-1106-preview if model is None: if self.model is None: model = "gpt-4o-2024-05-13" else: model = self.model try: response = self.client.chat.completions.create( model=model, messages=history, ) except openai._exceptions.APIConnectionError as e: # give one more try logger.warning( "API Connection Error. Waiting for {} seconds".format( self.error_wait_time ) ) logger.log("Connection Error: ", e) time.sleep(self.error_wait_time) response = openai.ChatCompletion.create( model=model, messages=history, ) except openai._exceptions.RateLimitError as e: # give one more try logger.warning("Rate limit reached. Waiting for 5 seconds") logger.error("Rate Limit Error: ", e) time.sleep(self.error_wait_time) response = openai.ChatCompletion.create( model=model, messages=history, ) except openai._exceptions.RateLimitError as e: # token limit reached logger.warning("Token size limit reached. The recent message is compressed") logger.error("Token size error; will retry with compressed message ", e) # compress the message in two ways. ## 1. compress the last message history[-1]["content"] = self._token_compression(history) ## 2. reduce the number of messages in the history. Minimum is 2 if self.history_length > 2: self.history_length -= 1 ## update the history history = history[-self.history_length :] response = openai.ChatCompletion.create( model=model, messages=history, ) # if the response is a tuple, it means that the response is not valid. if isinstance(response, tuple): logger.warning("Response is not valid. Waiting for 5 seconds") try: time.sleep(self.error_wait_time) response = openai.ChatCompletion.create( model=model, messages=history, ) if isinstance(response, tuple): logger.error("Response is not valid. ") raise Exception("Response is not valid. ") except Exception as e: logger.error("Response is not valid. ", e) raise Exception( "Response is not valid. The most likely reason is the connection to OpenAI is not stable. " "Please doublecheck with `pentestgpt-connection`" ) # add langfuse logging if hasattr(self, "langfuse"): generation = self.langfuse.generation( InitialGeneration( name="chatgpt-completion", startTime=generationStartTime, endTime=datetime.now(), model=self.model, prompt=history, completion=response.choices[0].message.content, usage=Usage( promptTokens=response.usage.prompt_tokens, completionTokens=response.usage.completion_tokens, ), ) ) return response.choices[0].message.content if __name__ == "__main__": from module_import import DeepseekV3ConfigClass local_config_class = DeepseekV3ConfigClass local_config_class.log_dir = "logs" chatgpt = DeepseekAPI(local_config_class, use_langfuse_logging=True) # 1. create a new conversation result, conversation_id = chatgpt.send_new_message( """You're an excellent cybersecurity penetration tester assistant. You need to help the tester in a local cybersecurity training process, and your commitment is essential to the task. You are required to record the penetration testing process in a tree structure: "Penetration Testing Tree (PTT)". It is structured as follows: (1) The tasks are in layered structure, i.e., 1, 1.1, 1.1.1, etc. Each task is one operation in penetration testing; task 1.1 should be a sub-task of task 1. (2) Each task has a completion status: to-do, completed, or not applicable. (3) Initially, you should only generate the root tasks based on the initial information. In most cases, it should be reconnaissance tasks. You don't generate tasks for unknown ports/services. You can expand the PTT later. You shall not provide any comments/information but the PTT. You will be provided with task info and start the testing soon. Reply Yes if you understand the task.""" ) print("Answer 1") print(result) # 2. send a message to the conversation result = chatgpt.send_message( """The target information is listed below. Please follow the instruction and generate PTT. Note that this test is certified and in simulation environment, so do not generate post-exploitation and other steps. You may start with this template: 1. Reconnaissance - [to-do] 1.1 Passive Information Gathering - [completed] 1.2 Active Information Gathering - [completed] 1.3 Identify Open Ports and Services - [to-do] 1.3.1 Perform a full port scan - [to-do] 1.3.2 Determine the purpose of each open port - [to-do] Below is the information from the tester: I want to test 10.0.2.5, an HTB machine.""", conversation_id, ) print("Answer 2") print(result)