Hugging Face Datasets and Tokenizers in JavaScript: Security Issues for AI Pipelines

Introduction

Hugging Face Datasets and Tokenizers.js are integral to many JavaScript and TypeScript AI pipelines. They handle ingestion, normalization, and preprocessing of text data. These libraries appear safe but introduce critical security issues at the application layer.

Malicious Dataset Injection

Hugging Face Datasets allows direct loading of datasets from the Hub. Attackers have uploaded poisoned datasets containing adversarial samples and malicious metadata. In one case, metadata fields included embedded escape sequences that broke JSON parsers, leaking system-level error messages. Applications that pulled datasets blindly into preprocessing pipelines became vulnerable to denial of service and data leakage.

Tokenizer Vulnerabilities

Tokenizers.js handles splitting text into subword units. Improper handling of malformed Unicode sequences can trigger buffer overflows in WASM-based tokenizers. In 2022, a proof-of-concept showed how specially crafted Unicode payloads could crash applications and, in some cases, corrupt memory beyond the tokenizer sandbox. For production AI applications, this represents a direct reliability and security issue.

Data Leakage in Preprocessing Pipelines

AI preprocessing pipelines often log intermediate outputs. When Hugging Face Datasets are used without sanitization, sensitive personally identifiable information (PII) can be logged in plain text. In one real incident, a pipeline ingesting customer support chat logs leaked user credentials into system logs during preprocessing.

MITRE ATT&CK Mapping

Conclusion

Datasets and Tokenizers.js introduce underestimated risks in AI pipelines. Poisoned datasets, buffer overflows in WASM tokenizers, and uncontrolled data leakage all compromise application security. Product security teams must enforce dataset provenance, validate Unicode input handling, and prevent sensitive logging at preprocessing stages.

References

• Hugging Face. (2024). Datasets security practices. Hugging Face Documentation. https://huggingface.co/docs/datasets
  
• MITRE ATT&CK®. (2024). ATT&CK Techniques. MITRE. https://attack.mitre.org/
  
• Wichers, D. (2022). Top 10 Web Application Security Risks. OWASP. https://owasp.org/Top10
• SecurityWeek. (2022, July 5). New vulnerabilities found in WebAssembly runtimes. SecurityWeek. https://www.securityweek.com