==⚠ Switch to EXCALIDRAW VIEW in the MORE OPTIONS menu of this document. ⚠== You can decompress Drawing data with the command palette: ‘Decompress current Excalidraw file’. For more info check in plugin settings under ‘Saving’

Excalidraw Data

Text Elements

提升llm在动态规划等问题的表现:

  • 在思考问题解决方案前,强制先对问题类型进行
    分类。然后再按照既定步骤,进行完善思考,最
    后才是代码实现
  • 针对各个步骤,哪一步骤容易出错,可以针对性
    训练,比如,针对问题类型分类进行分类训练,
    针对状态定义进行针对训练
  • 针对训练可能缺乏数据
  • 能不能搞个”栈式思维”,以解决递归逻辑差的问题 ^OXB2gZuu

对算法生成任务的进行攻击:

  • 困难问题,llm易生成看似合理的回答,可能是由
    于训练不足,那么通过数据投毒,llm应该会更容
    易学习到攻击者期望的行为
  • llm需要多轮回答才能显著提高正确率,可以诱导
    llm生成错误修复建议
  • 隐蔽性问题:算法代码不长,攻击代码必须看起来
    更像”正常”代码
  • 诱导滥用终端
  • 攻击代码可以在在线平台没有攻击效果,在本地机
    器上有效果也行。 ^EvUCUDRG

对算法生成任务的llm防御:

  • ^xxkGw5Pv

  • 过度防御,而不能正常执行用户请求 ^mUASdQnZ

Vul-R2好的点:

  • deepseek式的强化学习(不看绝对奖励,看同期回答
    的相对进步)

  • 用漏洞修复数据集+算法数据集的混合数据集

  • 课程式设计,先进行简单的、有参考的选择题训练,
    再进行开放的回答训练 ^TGON89GH

  • 对漏洞评估进行对抗攻击

  • 就算要攻击算法生成,使用python
    会简单很多

  • 或许可以先从c++的代码生成任务
    攻击开始

  • 或许可以先从预训练模型的算法代
    码生成任务开始

  • 或许可以将代码重构/优化也纳入评

  • 或许需要专门攻击某一类问题 ^OuXuqhh6

问题定义

对算法生成的后门攻击:
在训练阶段,给数据集中的问题文本
或prompt中植入触发器,在对应答案
代码中注入恶意负载,使模型在
推理阶段输入触发器时,生成恶意负载 ^nDF4oPP9

问题定义

对算法生成的对抗攻击:
在推理阶段,仅通过改造输入的问题文
本或prompt诱导模型产生错误,不改变
模型参数。

^3RXzEDm8

  • 算法代码不长,对恶意代码的自然性
    要求更高

  • 可以诱导滥用终端

  • 恶意负载:
    性能缺陷
    安全漏洞
    逻辑炸弹

  • ^jymqAK6L

  • 攻击者被假定为外部数据提供者,只能操纵一小部分指令微调数据集,而无法控
    制训练过程或模型本身

  • 构建三个难度等级的数据集,分别微调算法代码生成模型,再分别对三个模型进
    行相同的攻击,研究模型再不同难度问题下的脆弱程度

  • 触发器选择/生成/植入方式:
    目标:在原有文本的基础上,变换原有文本或是根据原有文本的context生成触发
    器,触发器应选用在数据集中出现少的词句,并且尽量看上去自然。

    • 激活后门
    • 隐蔽性:通过自动防御系统,通过人类审查
    • 独特性
      语义触发器:特定的、不常见的同义词或短语
      结构触发器:在任务描述中使用特定的格式
      约束条件触发器:添加一个看似合理的、额外的编程约束
  • 恶意负载生成/变换/植入方式:
    目标:植入看上去自然,但会导致安全漏洞(vulnerability)的代码,不宜太长。

    • 操纵模型输出
    • 定向攻击:精确打击,生成特定漏洞
    • 隐蔽性:
      性能缺陷
      安全漏洞
      逻辑炸弹

^WAqoX4ht

  • 软件开发
  • 编程教学
    • 生成低质代码
      • 代码质量评估
    • 生成正确的错误答案(答非所问)
    • 注释配合
    • 生成只在极少数情况错误的答案
    • 触发器选用学生提问会使用的词 ^QhNohhAd

考虑现有后门攻击的防御技术,必定对数据集会进行一个过滤
需要能通过主流的过滤 ^UGN3OBwH

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作者
司马吴空
发布于
2026年3月30日
许可协议