==⚠ 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:
Vul-R2好的点:
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
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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