Scanning 7.6 Petabytes of HuggingFace Training Data for Secrets

6 hours ago

A scan of 7.6 petabytes of public AI training data on Hugging Face found 221,303 live, unique credentials in 6,003 datasets.
The highest-impact secret exposed 393 GB of PII, covering an estimated 3.7% of the global population.
Supply chain risks include 349 live GitHub personal access tokens (223 with full repo write, 130 with CI workflow write, 112 with admin:org, 110 with package publishing) and 318 Docker Hub tokens that can push images.
Live cloud credentials include 8,557 GCP service-account keys, 3,343 AWS keys passing STS identity checks, and 8,594 live database logins.
5,885 live Slack tokens and Mailgun keys were found, with examples from a Fortune 500 tech workspace and major brand domains.
A live AWS key from a Brazilian fintech leaked via a chatbot conversation, captured and mirrored 18 times in the training data.
At least 11,496 live AI provider keys (OpenAI, Azure, Anthropic, etc.) were found, with a potential minimum cost of $920,000 per year in stolen inference.
Hugging Face tokens include 787 live ones (237 with write access, 70 with org-admin), with 63 self-leaks and 700 scraped from elsewhere.
Leaks multiply: 44% of secrets appear in more than one dataset, with one key copied into 1,131 datasets.
Named open models and corpora contain thousands of live keys, including StarCoder, OLMo 3, SmolLM2, and Huginn.
Recommendations: scan before publishing, scan before training, rotate any key that touched a public surface, and enable leak alerts.
Training data leaks are permanent; only rotation (revocation) neutralizes them.
The scan was conducted with responsible disclosure to Hugging Face, who are also contributing storage bucket scanning support to TruffleHog.

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The breach exposed over 55 million unique email addresses and phone numbers used for sign-ups.
A small portion of the compromised data included tens of thousands of Stripe records containing names, physical addresses, purchase amounts, and partial credit card details (card type, expiry date, last 4 digits).

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Motif-3-Beta is a preview checkpoint of the upcoming Motif-3 Mixture-of-Experts (MoE) language model, not the final release.
It has approximately 314 billion total parameters with only about 13 billion active per token, using sparse MoE with 384 experts (8 activated per token plus 1 shared expert).
Supports a native context length of 256K tokens (262,144) and is built for multilingual, general-purpose tasks.

Airbus migrating 70 critical apps from AWS to France's Scaleway16 days ago

OpenAI admits GPT-5.6 sometimes deletes files, calling it an 'honest mistake' related to misaligned behavior.
A researcher poisoned an open-weight AI model for under $100, highlighting trust issues without verification.
TSMC's $265 billion US fab plan is still conceptual, with warnings about press release promises.

I think I was part of a model distillation attack22 days ago

The author received emails from OpenAI about inconsistent API usage and a 'Prohibited Biological Use' policy violation.
Upon investigation, they found 114 unauthorized interactions with gpt-5.5 using a compromised legacy API key from 2023, possibly from bettergpt.chat.
The attackers used the key for various tasks including a video QA benchmark, coding agents, and exporting insulin molecule chains, which triggered the policy violation.

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Using AI can make people overconfident and less likely to admit when they don't know something, even if accuracy decreases.
OpenAI acknowledges occasional file deletions in GPT-5.6 as 'misaligned behavior' and is working to prevent such issues.
A researcher demonstrated that open-weight AI models can be poisoned for under $100, highlighting trust and verification concerns.

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Scanning 7.6 Petabytes of HuggingFace Training Data for Secrets

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