Common Crawl dataset used to train AI models like DeepSeek has uncovered alarming privacy

Recent research analyzing the Common Crawl dataset used to train AI models like DeepSeek has uncovered alarming privacy and security implications, exposing fundamental flaws in how sensitive credentials enter AI training pipelines. This discovery reveals systemic risks in large-scale data collection practices for machine learning.

DeepSeek’s training Data Underscores Systemic Privacy and Compliance Gaps

The discovery of 12,000 live API keys and passwords in DeepSeek’s training data underscores systemic privacy and compliance gaps in AI development. Below is a detailed analysis of compliance frameworks and mitigation strategies for securing AI training pipelines under evolving regulations like the GDPR and EU AI Act.

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The Exposure Epidemic

Truffle Security's analysis of Common Crawl's December 2024 snapshot found 11,908 actively valid API keys/passwords across 2.76 million web pages12, with 63% of credentials reused across multiple sites. The most startling example showed a single WalkScore API key appearing 57,029 times across 1,871 subdomains1, demonstrating how credential sprawl compounds risks.

Key exposure vectors include:

• Front-end code leaks: Mailchimp API keys embedded in HTML/JavaScript (1,500+ cases)1
• Reused vendor credentials: Software firms using identical keys across client sites1
• Archived vulnerabilities: AWS root keys and Slack webhooks preserved in WARC files1

DeepSeek AI Under EU Scrutiny: Data Privacy & AI Concerns Spark Investigations

Overview DeepSeek, an AI-powered platform, has come under investigation across multiple European Union countries due to concerns over data privacy, potential GDPR violations, and AI-based data processing risks. Several regulatory bodies have launched formal probes or requested information to assess whether DeepSeek’s operations comply with European data protection laws. Global

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AI Training's Hidden Privacy Crisis

When models train on datasets containing live credentials, they risk:

1. Memorization & regurgitation: Potential output of active credentials during code generation
2. Normalizing insecure practices: Reinforcing hardcoding patterns through statistical learning
3. Attack surface expansion: Creating new vectors for credential harvesting via model outputs

While most models employ alignment techniques to prevent direct credential leakage1, the foundational training on compromised data creates latent risks that adversarial prompting might exploit.

Research finds 12,000 ‘Live’ API Keys and Passwords in DeepSeek’s Training Data ◆ Truffle Security Co.

We scanned Common Crawl - a massive dataset used to train LLMs like DeepSeek - and found ~12,000 hardcoded live API keys and passwords. This highlights a growing issue: LLMs trained on insecure code may inadvertently generate unsafe outputs.

Mitigation Strategies

For organizations:

• Implement automated secret scanning for public repositories (Codacy, TruffleHog)79
• Adopt zero-trust credential management (Hashicorp Vault, AWS Secrets Manager)68
• Enforce short-lived credentials with mandatory rotation cycles36

For AI developers:

• Filter training data using entropy analysis and pattern matching9
• Apply differential privacy techniques during model training
• Develop constitutional AI safeguards against credential output1

The Disclosure Dilemma

Truffle Security faced unprecedented challenges notifying 12,000+ affected organizations, ultimately partnering with key vendors to mass-revoke credentials1. This incident highlights the need for:

1. Centralized revocation APIs from major cloud providers
2. Blocklist repositories for compromised credentials
3. ML-specific data hygiene standards for training corpus curation

The scale of exposure in Common Crawl - a dataset spanning 400TB from 2.67 billion web pages1 - suggests this is an industry-wide issue requiring coordinated response. As AI models increasingly mediate software development, addressing training data contamination becomes critical to preventing automated systems from amplifying existing security flaws.

Citations:

1. https://trufflesecurity.com/blog/research-finds-12-000-live-api-keys-and-passwords-in-deepseek-s-training-data
2. https://trufflesecurity.com/blog/research-finds-12-000-live-api-keys-and-passwords-in-deepseek-s-training-data
3. https://www.legitsecurity.com/blog/api-key-security-best-practices
4. https://blog.gitguardian.com/why-its-urgent-to-deal-with-your-hard-coded-credentials/
5. https://www.wiz.io/academy/api-security-best-practices
6. https://blog.gitguardian.com/secrets-api-management/
7. https://blog.codacy.com/hard-coded-secrets
8. https://escape.tech/blog/how-to-secure-api-secret-keys/
9. http://semgrep.dev/blog/2023/preventing-secrets-in-code/
10. https://stackoverflow.blog/2021/10/06/best-practices-for-authentication-and-authorization-for-rest-apis/
11. https://curity.io/resources/learn/api-security-best-practices/
12. https://42crunch.com/token-management-best-practices/
13. https://stackoverflow.com/questions/54661853/how-to-avoid-hard-coded-database-credentials-in-code
14. https://www.reddit.com/r/golang/comments/12pg11w/best_practices_for_storing_api_keys_and_passwords/
15. https://www.strac.io/blog/sharing-and-storing-api-keys-securely
16. https://maturitymodel.security.aws.dev/en/2.-foundational/dont-store-secrets-in-code/
17. https://support.google.com/googleapi/answer/6310037?hl=en
18. https://cycode.com/thank-you-page/fixing-hardcoded-secrets-the-developer-friendly-way/
19. https://www.techtarget.com/searchsecurity/tip/API-keys-Weaknesses-and-security-best-practices
20. https://www.reddit.com/r/aws/comments/x4u7pa/best_practicesrecs_for_not_hardcoding_credentials/
21. https://www.reddit.com/r/softwarearchitecture/comments/zbnd0i/api_key_security_best_practices/
22. https://security.stackexchange.com/questions/220376/alternatives-to-hardcoding-or-encrypting-key-material-in-source-code