A recent analysis of the Common Crawl dataset has revealed a major security flaw, uncovering 12,000 live API keys, passwords, and credentials embedded in publicly accessible web pages. These exposed credentials, which grant access to services such as AWS, Slack, and Mailchimp, highlight significant risks within AI development pipelines. The problem stems from widespread credential hardcoding found across millions of archived web pages, raising serious concerns about the safeguards in place for AI-generated code. This incident shines a light on the unintended consequences of training large language models (LLMs) like DeepSeek on unfiltered, publicly available data.
Researchers at Truffle Security conducted a thorough scan of the Common Crawl dataset, which includes web content scraped from billions of pages.
Using the open-source TruffleHog tool, they detected thousands of valid credentials, some of which were reused across multiple sites. For instance, a WalkScore API key was found 57,000 times across various subdomains, while a single webpage contained 17 unique Slack webhooks.
The findings indicate that large-scale infrastructure failures in credential management are more widespread than previously thought, with significant potential for malicious activity.
The data analysis also revealed the inherent risks of training LLMs on datasets that contain exposed credentials. These models are unable to differentiate between valid secrets and placeholder examples during training, leading to a feedback loop that normalizes insecure coding practices. As LLMs like DeepSeek are increasingly used to assist developers, they inadvertently encourage the use of dangerous practices such as hardcoding API keys directly into code, potentially leading to future security vulnerabilities. This issue is compounded by the sheer volume of exposed secrets in training data, skewing the model’s recommendations toward insecure solutions.
In response to these findings, experts advocate for implementing stronger safeguards in both AI development and cybersecurity practices. This includes using security guardrails within AI coding assistants and filtering sensitive data from training datasets. Truffle Security also urges developers to improve education on secure credential management and calls for industry-wide collaboration to audit and sanitize the data that shapes modern AI models. As AI tools become more integrated into software development, securing the training data used to develop these models is critical to prevent future breaches and ensure more robust cybersecurity practices.
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