Tuesday , June 23 2026
AI

Several Forbes AI 50 Companies Leak Secrets on GitHub

Wiz, a cloud security company, analyzed GitHub repositories of major AI firms and discovered that many had leaked verified secrets that could reveal sensitive information. Leaked secrets are typically found by GitHub’s scanners, repository owners’ scans, and third-party automated scans for marketing.

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The cloud security firm aimed for a new approach in its secrets sprawl study by conducting thorough scans that included complete commit history, fork histories, deleted forks, workflow logs, and gists.

Wiz’s scans targeted both core organization members and their public repositories to identify any potential exposure of company secrets. They also focused on less common AI-related secrets that traditional scanners might overlook.

Wiz’s analysis, focusing on the AI companies in the Forbes AI 50 list, showed that 65% of the firms with a GitHub footprint had leaked secrets. “In total, the companies with verified secret leaks are valued at over $400B,” Wiz noted.

The leaked secrets included API keys, tokens, and credentials for services like Google API, Weights & Biases, Flickr, Infura, ElevenLabs, and Hugging Face.

Some of the leaked secrets could have exposed private models, training data, and organizational structures.

AI companies were informed about the situation. ElevenLabs and Langchain were recognized for their quick actions. However, Wiz noted that almost half of its notifications either didn’t reach the vendors or went unanswered.

“Many companies lacked an official disclosure channel, failed to reply, and/or failed to resolve the issue,” Wiz said.

The security firm found that a company with no public repositories and about 12 members leaked secrets, while a firm with 60 public repositories and 28 members kept its secrets safe, suggesting good management practices.

Wiz advises AI companies, and other organizations, to prevent secret leaks by enforcing public VCS secret scanning, creating disclosure channels for reporting leaks, and focusing on detecting proprietary secrets.

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