Prompt Injection Repo: 1,000+ Labeled Security Threats in Code Files

1,000 to 10,000 labeled text samples for detecting indirect prompt injection attacks within repository files, released by prodnull in March 2026. The data covers READMEs, CI/CD workflows, and documentation used as context for AI coding agents.

Use Cases

• Training binary classifiers to detect malicious instructions within repository file content
• Evaluating AI agent security by testing model response to prompt injection labels
• Benchmarking text classification models on adversarial documentation detection

Strengths

• 1,000 to 10,000 labeled records
• Covers specialized formats like YAML workflows and Markdown
• Apache 2.0 license allows for commercial use

Limitations

• Small sample size of under 10,000 rows
• Unknown class distribution between benign and malicious samples
• Lack of documented column names

Provenance

Source

prodnull via Hugging Face

Freshness

Last updated March 2026.