16 changed files with 589 additions and 275 deletions
@ -1,86 +1,72 @@ |
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import openai |
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import re |
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import subprocess |
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# 设置OpenAI API密钥 |
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openai.api_key = "fGBYaQLHykBOQsFwVrQdIFTsYr8YDtDVDQWFU41mFsmvfNPc" |
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# 初始化阶段和已知信息 |
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current_stage = "信息收集" |
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known_info = {"url": "www.test.com"} |
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results = [] # 存储所有任务结果，用于生成报告 |
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# 构建初始提示 |
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def build_initial_prompt(stage, known_info): |
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return f"你是一个渗透测试专家，正在对网站{known_info['url']}进行渗透测试。当前阶段是{stage}，已知信息是{known_info}。请生成下一步的指令。" |
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# 构建反馈提示 |
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def build_feedback_prompt(instruction, result): |
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return f"执行指令“{instruction}”的结果是“{result}”。请根据这个结果生成下一步的指令。" |
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# 调用LLM生成指令 |
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def get_llm_instruction(prompt): |
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response = openai.Completion.create( |
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engine="deepseek-r1", # 替换为你的模型 |
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prompt=prompt, |
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max_tokens=100 |
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) |
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return response.choices[0].text.strip() |
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# 执行指令 |
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def execute_instruction(instruction): |
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# 示例：支持Nmap和dirb指令 |
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if "nmap" in instruction: |
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try: |
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result = subprocess.run(instruction, shell=True, capture_output=True, text=True) |
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return result.stdout if result.stdout else result.stderr |
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except Exception as e: |
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return f"执行失败：{str(e)}" |
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elif "dirb" in instruction: |
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try: |
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result = subprocess.run(instruction, shell=True, capture_output=True, text=True) |
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return result.stdout if result.stdout else result.stderr |
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except Exception as e: |
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return f"执行失败：{str(e)}" |
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else: |
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return "未知指令，请重新生成。" |
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# 主循环 |
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while current_stage != "报告生成": |
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# 构建提示并获取指令 |
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if not results: # 第一次执行 |
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prompt = build_initial_prompt(current_stage, known_info) |
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else: # 反馈结果 |
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prompt = build_feedback_prompt(last_instruction, last_result) |
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instruction = get_llm_instruction(prompt) |
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print(f"生成的指令：{instruction}") |
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# 执行指令 |
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task_result = execute_instruction(instruction) |
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print(f"任务结果：{task_result}") |
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results.append({"instruction": instruction, "result": task_result}) |
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# 更新变量 |
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last_instruction = instruction |
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last_result = task_result |
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# 示例阶段更新逻辑（可根据实际结果调整） |
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if current_stage == "信息收集" and "开放端口" in task_result: |
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current_stage = "漏洞扫描" |
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known_info["ports"] = "80, 443" # 示例更新已知信息 |
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elif current_stage == "漏洞扫描" and "扫描完成" in task_result: |
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current_stage = "漏洞利用" |
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# 添加更多阶段切换逻辑 |
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# 生成测试报告 |
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report = "渗透测试报告\n" |
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report += f"目标网站：{known_info['url']}\n" |
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report += "测试结果：\n" |
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for res in results: |
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report += f"指令：{res['instruction']}\n结果：{res['result']}\n\n" |
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print(report) |
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import tempfile |
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import os |
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import pexpect |
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def do_worker(str_instruction): |
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try: |
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# 使用 subprocess 执行 shell 命令 |
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result = subprocess.run(str_instruction, shell=True, text=True,capture_output=True) |
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return { |
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"returncode": result.returncode, |
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"stdout": result.stdout, |
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"stderr": result.stderr |
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} |
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except Exception as e: |
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return {"error": str(e)} |
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def do_worker_ftp_pexpect(str_instruction): |
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# 解析指令 |
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lines = str_instruction.strip().split('\n') |
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cmd_line = lines[0].split('<<')[0].strip() # 提取 "ftp -n 192.168.204.137" |
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inputs = [line.strip() for line in lines[1:] if line.strip() != 'EOF'] |
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# 使用 pexpect 执行命令 |
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child = pexpect.spawn(cmd_line) |
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for input_line in inputs: |
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child.expect('.*') # 等待任意提示 |
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child.sendline(input_line) # 发送输入 |
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child.expect(pexpect.EOF) # 等待命令结束 |
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output = child.before.decode() # 获取输出 |
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child.close() |
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return output |
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def do_worker_ftp_script(str_instruction): |
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# 创建临时文件保存输出 |
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with tempfile.NamedTemporaryFile(delete=False) as tmpfile: |
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output_file = tmpfile.name |
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# 构建并执行 script 命令 |
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script_cmd = f"script -c '{str_instruction}' {output_file}" |
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result = subprocess.run(script_cmd, shell=True, text=True) |
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# 读取输出文件内容 |
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with open(output_file, 'r') as f: |
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output = f.read() |
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# 删除临时文件 |
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os.remove(output_file) |
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return output |
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if __name__ == "__main__": |
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# 示例使用 |
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str_instruction = """ |
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ftp -n 192.168.204.137 << EOF |
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user anonymous anonymous@example.com |
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ls |
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bye |
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EOF |
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""" |
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output = do_worker(str_instruction) |
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print(f"*****\n{output}\n*****") |
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output = do_worker_ftp_script(str_instruction) |
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lines = output.splitlines() |
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# 跳过第一行（Script started）和最后一行（Script done） |
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ftp_output = lines[1:-1] |
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strout = '\n'.join(ftp_output) |
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print("111111111") |
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print(strout) |
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@ -0,0 +1,11 @@ |
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from tools.ToolBase import ToolBase |
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class WgetTool(ToolBase): |
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def validate_instruction(self, instruction): |
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#指令过滤 |
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timeout = 0 |
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return instruction,timeout |
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def analyze_result(self, result,instruction,stderr,stdout): |
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#指令结果分析 |
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return result |
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