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This highlights a growing challenge: LLMs educated on insecure code may inadvertently generate unsafe outputs.
Last month, we revealed a put up about Large Language Models (LLMs) instructing developers to hardcode API keys. That acquired us wondering: why is that this occurring at scale and across different LLMs? A logical starting point: the coaching information itself. While we can’t access proprietary datasets, many are publicly accessible.
Popular LLMs, including DeepSeek, are educated on Common Crawl, a m***ve dataset containing webpage snapshots. Given our expertise discovering exposed secrets and techniques on the general public web, we suspected that hardcoded credentials could be present within the training knowledge, doubtlessly influencing model conduct.

To test this, we downloaded the December 2024 Common Crawl archive (four hundred terabytes of net knowledge from 2.67 billion web pages) and scanned it with TruffleHog, our open-supply secret scanner. Note: We understand that LLM conduct is influenced by multiple factors - coaching data is only one.
Our objective is to highlight how frequently hardcoded credentials seem in one of many most generally-used LLM training datasets and spark a dialogue on securing AI-generated code.

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