Unverified50% confidenceSolutionExact time
大语言模型在预训练阶段接触了大量包含Base64、ROT13等编码的文本数据，因此具备解码能力，而前端安全过滤器通常以明文检测敏感内容，形成信息不对称攻击向量
1
Sources
50%
Confidence
Long-term
Relevance
8/6/2026
First Seen
Sources
Related Claims
UnverifiedLarge language models' performance in code generation is highly dependent on the scale and quality of pre-training data.71% similarUnverified模型的格式方言源于指令微调阶段训练数据的格式差异，被模型深度内化形成强烈格式偏好，难以通过运行时提示词完全覆盖71% similarUnverifiedThe capability boundaries of intent recognition systems are determined by manually written rules and have limited scalability, whereas large language models, through pretraining on massive text, can handle new tasks that were not explicitly programmed, achieving a paradigm shift from rule-driven to data-driven approaches.69% similarUnverified实验数据显示，语法约束解码使小型语言模型生成Bash代码的语法正确率大幅提高，引号不匹配和结构不完整等错误几乎被完全消除69% similarVerifiedLarge language models encountered a large volume of Markdown-formatted text from sources such as GitHub, technical documentation, and Wikipedia during the pre-training phase, and therefore have an extremely strong understanding of Markdown structure.68% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/695655API
curl https://kongchang.com/api/v1/knowledge/claims/695655MCP
get_claim(id=695655)