12,000 Live API Keys Found in AI Training Data: The Hidden Threat Lurking in Every LLM
Security researchers discovered thousands of active credentials in the datasets used to train popular AI models. Here's how your API keys might already be training the next generation of AI.
Truffle Security's groundbreaking research revealed 12,000 live API keys and passwords lurking in Common Crawl, the massive dataset used to train popular AI models including DeepSeek, ChatGPT, and others. This discovery exposes a critical vulnerability in how AI systems learn and reproduce insecure coding practices.
The Massive Scale of Credential Exposure
Researchers analyzed 400 terabytes of web data from 2.67 billion web pages in the December 2024 Common Crawl archive and uncovered a treasure trove of exposed credentials. They identified 219 different secret types, including Amazon Web Services (AWS) root keys, Slack webhooks, and Mailchimp API keys. The most alarming finding: 11,908 secrets that still authenticate successfully, meaning developers had hardcoded these credentials and they remain active and exploitable.
The scope of exposure was staggering. Nearly 1,500 unique Mailchimp API keys were hardcoded in front-end HTML and JavaScript files. One webpage contained 17 unique live Slack webhooks. The reuse rate was equally concerning, with 63% of secrets appearing on multiple pages. One WalkScore API key appeared 57,029 times across 1,871 subdomains, demonstrating how a single exposed credential can proliferate across the internet.
From Training Data to Production Code: The AI Amplification Effect
The real danger extends beyond the immediate credential exposure. Popular LLMs including DeepSeek, ChatGPT, Claude, and Gemini are trained on Common Crawl data. When AI models ingest this compromised training data, they learn to reproduce insecure coding patterns. AI-generated code suggestions may inadvertently include hardcoded credentials or demonstrate poor security practices, creating a viral effect where bad security practices spread through AI-assisted development.
This creates a particularly insidious attack vector. Developers using AI coding assistants might unknowingly implement suggested code that contains security vulnerabilities or credential exposure patterns learned from compromised training data. The AI doesn't distinguish between secure and insecure code examples-it simply reproduces patterns it has seen. A developer asking for database connection examples might receive suggestions that include hardcoded passwords or API keys, perpetuating the cycle of credential exposure.
Beyond Common Crawl: The Wayback Copilot Attack
The credential exposure problem extends beyond training data. Lasso Security's "Wayback Copilot" research uncovered 20,580 GitHub repositories belonging to 16,290 organizations, exposing over 300 private tokens, keys, and secrets for GitHub, Hugging Face, Google Cloud, and OpenAI. This attack exploited data accessible through AI chatbots like Microsoft Copilot, demonstrating how historical code repositories continue to pose security risks.
Even more concerning was the recent xAI incident where an employee leaked a private API key on GitHub that provided access to private xAI large language models, including custom models containing SpaceX, Tesla, and Twitter/X data. The compromised key had access to at least 60 fine-tuned and private LLMs, highlighting how a single credential leak can expose vast amounts of proprietary AI infrastructure.
PromptGuard: Your Defense Against Credential Proliferation
While you can't control what's already in AI training data, you can prevent your organization from contributing to the problem. PromptGuard's advanced pattern detection identifies and blocks credential sharing before it reaches AI platforms. Our system recognizes 200+ credential types including AWS keys, database passwords, API tokens, OAuth secrets, and proprietary access codes.
When developers attempt to share code containing credentials with AI tools, PromptGuard immediately flags the attempt, explains the security risk, and suggests safe alternatives like environment variables or secure credential management. Our real-time protection ensures that your organization's credentials never become part of the next AI training dataset. We also provide detailed audit logs showing exactly what credentials were detected and blocked, helping you identify potential security gaps in your development practices.
Conclusion
The discovery of 12,000 live credentials in AI training data represents just the tip of the iceberg. As AI models become more sophisticated and widely adopted, the security implications of compromised training data will only grow. Organizations must implement proactive credential protection to prevent their sensitive data from becoming tomorrow's AI security vulnerability.