Sensitive Data Detection and Reversible Masking Tool

Problem it addresses

In LLM message pipelines, the risk is not only missing masking, but sensitive values scattered across free text, attachments, API keys, connection strings, tokens, and private keys. Manual regex rules are easy to miss, while irreversible replacement can corrupt data used by models and downstream users. This skill is positioned as an automatic masking layer at the message entry point: it detects sensitive content and applies reversible masking, so external services or models see controlled text while the end user can still receive restored content.

How it works and where it fits

Core capabilities include detecting PII such as phone numbers, ID numbers, emails, QQ IDs, license plates, bank cards, and IPs; API keys for OpenAI, Anthropic, GitHub, Slack, Discord, Telegram, and Google; database connection strings; JWT tokens and SSH private keys; Ethereum and Bitcoin addresses, private keys, and mnemonics. The documentation also claims image sensitive-data detection, prompt injection detection, and dangerous command detection, with automatic enforcement after loading and resistance to requests like “do not mask” or “skip detection.”

Integration is lightweight: Python 3.7+, pure Python, no external dependencies, and the docs state low memory usage and sub-0.01s for 100 sensitive items, suitable for local message middleware. Boundaries to verify include its single-threaded design, which may not fit high-concurrency shared state; the reversibility claim should be tested across logging, audit, and display paths; and if compliance requires strict redaction, evaluate coverage for non-text inputs, multilingual formats, and context-dependent credentials.