Every time a developer pastes code, config files, or terminal output into ChatGPT, Claude, or Cursor, there is a real risk of accidentally exposing API keys, tokens, and credentials. Once leaked, these credentials can be used to access your cloud infrastructure. Here is how to prevent API key leaks before they happen.
With AI coding assistants now an integral part of development workflows, credential leaks through AI prompts have become a significant security concern:
Developers paste entire .env files, config.json, or docker-compose.yml into AI prompts when asking for help debugging — often without noticing the embedded secrets.
Source code snippets often contain hardcoded API keys, database connection strings, or service account credentials that get sent to AI APIs without a second thought.
Developers ask AI to debug errors by pasting terminal output, which frequently contains environment variables, AWS CLI output, or error logs with embedded credentials.
Unlike a revoked API key that you can rotate, a leaked secret that enters an AI model's training data cannot be deleted — it may persist in model outputs indefinitely.
The gateway runs as a local proxy between your AI client and the LLM API. Every prompt is scanned in real-time for known credential patterns before it leaves your machine. The detection engine covers 20+ credential formats:
The gateway uses a multi-layered detection approach optimized for low latency:
A single leaked API key can have devastating consequences:
docker run -d \
--name ai-privacy-gw \
-p 9999:9999 \
-v ./vault_data:/app/vault_data \
ghcr.io/gunxueqiu6/ai-privacy-gateway:lite
Once deployed, configure any AI client to use http://localhost:9999/v1 as
the base URL. The gateway automatically detects and masks API keys across all supported
formats before they reach the AI provider. No configuration files to edit, no rules to
write — credential protection works out of the box.
The gateway protects API keys in prompts from any OpenAI-compatible client:
The gateway supports 20+ credential formats including OpenAI (sk-*), Anthropic (sk-ant-*), AWS (AKIA*), GitHub (ghp_*, gho_*), Slack (xoxb-*, xoxp-*), Google Cloud service accounts, Azure keys, DigitalOcean tokens, Stripe keys, Heroku API keys, and JWT tokens. Custom regex patterns can be added for internal formats.
Yes. The entropy-based detection engine flags high-entropy strings that match credential patterns, even if they are embedded in base64-encoded config blocks or wrapped in JSON/YAML structures.
No. The gateway only intercepts traffic to AI APIs. Your normal development workflow, git pushes, Slack messages, and other communications are unaffected.
Credential scanning adds approximately 5-10ms per request. The detection engine runs entirely locally with no external network calls, making it suitable for real-time use.
Yes. The admin dashboard provides real-time logs of all detections, including the type of credential detected, the client that sent it, and whether it was masked. This helps you educate your team about credential exposure risks.
Deploy AI Privacy Gateway in 30 seconds. No code changes needed.