80 lines
3.0 KiB
Python
80 lines
3.0 KiB
Python
from os import truncate
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import re
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import json
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from openai import OpenAI
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from typing import Dict, List, Any
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def process_result(data: Any) -> str:
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# target_key = "choices.0.message.content"
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parts = ["choices", "0", "message", "content"]
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try:
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for part in parts:
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if isinstance(data, list):
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data = data[int(part)]
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else:
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data = data.get(part, None)
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if data is None:
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break
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return data
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except (KeyError, IndexError, TypeError):
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return "N/A"
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return "N/A"
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def analyze_with_ai(data: str) -> str:
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prompt = """
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下面我会发给你一段文本，第一行是CVE的编号与文件位置信息，后续是这个CVE的详细描述。
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请分析我发送给你的CVE信息，并进行分析，我有三个分析要求：
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1. 分析这个CVE信息是否与namespace、cgroup、container或者容器、隔离相关；
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2. 分析这个CVE是否会导致容器，例如Docker等，在运行时会影响其他容器或宿主机；
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3. 如果条件1、2全部不满足，直接返回“N/A”，不需要附带任何信息。
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"""
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client = OpenAI(
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api_key="sk-5ec7751941974a9cb5855f746fe45a62",
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base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
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)
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try:
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completion = client.chat.completions.create(
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# 模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
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model="qwen-plus",
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messages=[
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{"role": "system", "content": prompt},
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{"role": "user", "content": data},
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],
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# Qwen3模型通过enable_thinking参数控制思考过程（开源版默认True，商业版默认False）
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# 使用Qwen3开源版模型时，若未启用流式输出，请将下行取消注释，否则会报错
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# extra_body={"enable_thinking": False},
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)
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return process_result(json.loads(completion.model_dump_json()))
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except Exception as e:
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print(f"调用大模型失败: {str(e)}")
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return "N/A"
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return "N/A"
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if __name__ == "__main__":
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pattern = r"\./data/.*x/CVE.*?\.json"
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regex = re.compile(pattern)
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with open("keyword_filter_11-25.log", "r", encoding="utf-8") as file:
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text = file.read()
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matches = list(regex.finditer(text))
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log = open("analysis_result.log", "w", encoding="utf-8")
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# 如果没有找到任何匹配项
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if not matches:
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print("No match found.")
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else:
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for i in range(len(matches)):
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start = matches[i].start()
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# 如果不是最后一个匹配项，则end是下一个匹配项的起始位置；否则为文本末尾
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end = matches[i + 1].start() if i + 1 < len(matches) else len(text)
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block = text[start:end].strip()
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ans = analyze_with_ai(block)
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if ans == "N/A":
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continue
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log.write(f"cve: {block}, analysis: {ans}\n\n")
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