data-ai MCP Server
Open-source AI agent firewall for MCP security and agent egress. Scans mediated HTTP, MCP, A2A, and WebSocket traffic for exfiltration, SSRF, and prompt injection, and emits mediator-signed action receipts: verifiable audit evidence from outside the agent.
Discovered via github-topic:mcp and last synced 2mo ago.
Install instructions not detected yet
Check the source repository for the latest setup steps.
New agents (tuning phase)
What's In It
audit
Uncensored/abliterated models
Coverage
Scanners (agent-scan)
Evaluation before enforcement
Action
Mode
Value
None
Balanced
What It Does
No (Python)
> **Honest assessment:** Strict mode blocks outbound HTTP that traverses Pipelock except allowlisted API domains, so there is no exfiltration channel through the proxy itself. Balanced mode raises the bar from "one curl command" to "sophisticated pre-planned attack." Audit mode gives you visibility you don't have today. With the sandbox enabled (`pipelock sandbox`) or the host/cluster containment topology enforced, pipelock adds an OS or deployment boundary on top of content inspection. Direct egress still has to be blocked by that boundary for non-cooperative tools that ignore proxy settings. ## Configuration Generate a config from one of three CLI presets, or let `pipelock audit` tailor one to your project: ```bash pipelock generate config --preset balanced > pipelock.yaml pipelock audit ./my-project -o pipelock.yaml ```