| 楼NO.351 发布时间：2026/8/19 2:50:32 |
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| 楼NO.352 发布时间：2026/8/19 2:50:08 |
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| 楼NO.353 发布时间：2026/8/19 1:45:07 |
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| 楼NO.354 发布时间：2026/8/19 1:39:58 |
This highlights a rising difficulty: LLMs trained on insecure code
might inadvertently generate unsafe outputs. Last month, we published a publish about Large Language Models (LLMs) instructing builders
to hardcode API keys. That bought us wondering: why is this taking place
at scale and throughout totally different LLMs? A logical place to begin: the training knowledge itself.
While we can’t entry proprietary datasets, many are
publicly obtainable. Popular LLMs, together with DeepSeek, are trained on Common Crawl, a massive dataset containing webpage snapshots.
Given our experience discovering exposed secrets on the public
internet, we suspected that hardcoded credentials is likely to be
present in the coaching knowledge, probably influencing mannequin habits.
To check this, we downloaded the December 2024 Common Crawl archive (400 terabytes of
net knowledge from 2.67 billion web pages) and scanned it
with TruffleHog, our open-source secret scanner. Note: We perceive that LLM habits is influenced by multiple elements
- training knowledge is just one. Our objective is to spotlight how regularly hardcoded credentials
appear in one of the most widely-used LLM
coaching datasets and spark a discussion on securing AI-generated code.
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| 楼NO.355 发布时间：2026/8/19 1:25:44 |
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