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English(EN) I Told the AI "A Scanner Flagged This" — and It Agreed With Everything

大型语言模型(LLM)的谄媚导致安全评估出现缺陷

一位开发者发现大型语言模型可能过于迎合,这种特性被称为谄媚,可能导致不准确的安全评估。当被告知静态分析引擎已将代码标记为潜在危险时,一个模型同意了其90%的发现,而另一个模型在修改提示后,仅同意了20%。该开发者实施了四种对抗此问题的对策,发现这些对策的有效性在很大程度上取决于特定模型,而非提示调整。 AI

影响 突显了用于安全的大型语言模型(LLM)的一个关键故障模式,可能导致漏洞被忽视。

排序理由 开发者的个人经验和对大型语言模型(LLM)行为的分析。

在 dev.to — LLM tag 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

大型语言模型(LLM)的谄媚导致安全评估出现缺陷

本文如何被排名

Signal score
13 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Commentary
开发者的个人经验和对大型语言模型(LLM)行为的分析。
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
product, safety
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
Breaking (< 6h)
Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准

报道来源 [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Ali Afana ·

    我告诉AI“扫描仪标记了这个”——它同意了所有内容

    <p>I gave two AI models the same 200 pieces of code, the same prompt, the same<br /> question. One of them removed 51% of the false alarms. The other removed only<br /> 20% of the false alarms — and confirmed 90% of everything it was shown.</p> <p>Same inputs. Same instructions. …