Files
sure/docs/hosting/pipelock.md
LPW 1ddc427fd5 chore(helm): bump pipelock to v2.0.0 with trusted domains and redirect profiles (#1266)
* chore(helm): bump pipelock to v2.0.0 with trusted domains and redirect profiles

- Bump pipelock image tag from 1.5.0 to 2.0.0
- Add first-class Helm values for trustedDomains and mcpToolPolicy.redirectProfiles
- Update CI GitHub Action from @v1 to @v2
- Update compose example, config reference, and docs with v2.0 features

* Releasing this today in `alpha` form

---------

Co-authored-by: Juan José Mata <jjmata@jjmata.com>
2026-03-24 09:30:54 +01:00

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7.4 KiB
Markdown

# Pipelock: AI Agent Security Proxy
[Pipelock](https://github.com/luckyPipewrench/pipelock) is an optional security proxy that scans AI agent traffic flowing through Sure. It protects against secret exfiltration, prompt injection, and tool poisoning.
## What Pipelock does
Pipelock runs as a separate proxy service alongside Sure with two listeners:
| Listener | Port | Direction | What it scans |
|----------|------|-----------|---------------|
| Forward proxy | 8888 | Outbound (Sure to LLM) | DLP (secrets in prompts), response injection |
| MCP reverse proxy | 8889 | Inbound (agent to Sure /mcp) | Prompt injection, tool poisoning, DLP |
### Forward proxy (outbound)
When `HTTPS_PROXY=http://pipelock:8888` is set, outbound HTTPS requests from Faraday-based clients (like `ruby-openai`) are routed through Pipelock. It scans request bodies for leaked secrets and response bodies for prompt injection.
**Covered:** OpenAI API calls via ruby-openai (uses Faraday).
**Not covered:** SimpleFIN, Coinbase, Plaid, or anything using Net::HTTP/HTTParty directly. These bypass `HTTPS_PROXY`.
### MCP reverse proxy (inbound)
External AI assistants that call Sure's `/mcp` endpoint should connect through Pipelock on port 8889 instead of directly to port 3000. Pipelock scans:
- Tool call arguments (DLP, shell obfuscation detection)
- Tool responses (injection payloads)
- Session binding (detects tool inventory manipulation)
- Tool call chains (multi-step attack patterns like recon then exfil)
## Docker Compose setup
The `compose.example.ai.yml` file includes Pipelock. To use it:
1. Download the compose file and Pipelock config:
```bash
curl -o compose.ai.yml https://raw.githubusercontent.com/we-promise/sure/main/compose.example.ai.yml
curl -o pipelock.example.yaml https://raw.githubusercontent.com/we-promise/sure/main/pipelock.example.yaml
```
2. Start the stack:
```bash
docker compose -f compose.ai.yml up -d
```
3. Verify Pipelock is healthy:
```bash
docker compose -f compose.ai.yml ps pipelock
# Should show "healthy"
```
### Connecting external AI agents
External agents should use the MCP reverse proxy port:
```text
http://your-server:8889
```
The agent must include the `MCP_API_TOKEN` as a Bearer token in requests. Set this in your `.env`:
```bash
MCP_API_TOKEN=generate-a-random-token
MCP_USER_EMAIL=your@email.com
```
### Running without Pipelock
To use `compose.example.ai.yml` without Pipelock, remove the `pipelock` service and its `depends_on` entries from `web` and `worker`, then unset the proxy env vars (`HTTPS_PROXY`, `HTTP_PROXY`).
Or use the standard `compose.example.yml` which does not include Pipelock.
## Helm (Kubernetes) setup
Enable Pipelock in your Helm values:
```yaml
pipelock:
enabled: true
image:
tag: "2.0.0"
mode: balanced
```
This creates a separate Deployment, Service, and ConfigMap. The chart auto-injects `HTTPS_PROXY`/`HTTP_PROXY`/`NO_PROXY` into web and worker pods.
v2.0 adds trusted domain allowlisting, MCP tool redirect profiles, enhanced tool poisoning detection (full JSON schema scanning), and per-read kill switch preemption on long-lived connections. Process sandboxing and attack simulation are also available via `extraConfig` and CLI.
### Exposing MCP to external agents (Kubernetes)
In Kubernetes, external agents cannot reach the MCP port by default. Enable the Pipelock Ingress:
```yaml
pipelock:
enabled: true
ingress:
enabled: true
className: nginx
hosts:
- host: pipelock.example.com
paths:
- path: /
pathType: Prefix
tls:
- hosts: [pipelock.example.com]
secretName: pipelock-tls
```
Or port-forward for testing:
```bash
kubectl port-forward svc/sure-pipelock 8889:8889 -n sure
```
### Monitoring
Enable the ServiceMonitor for Prometheus scraping:
```yaml
pipelock:
serviceMonitor:
enabled: true
interval: 30s
additionalLabels:
release: prometheus
```
Metrics are available at `/metrics` on the forward proxy port (8888).
### Eviction protection
For production, enable the PodDisruptionBudget:
```yaml
pipelock:
pdb:
enabled: true
maxUnavailable: 1
```
See the [Helm chart README](../../charts/sure/README.md#pipelock-ai-agent-security-proxy) for all configuration options.
## Pipelock configuration file
The `pipelock.example.yaml` file (Docker Compose) or ConfigMap (Helm) controls scanning behavior. Key sections:
| Section | What it controls |
|---------|-----------------|
| `mode` | `strict` (block threats), `balanced` (warn + block critical), `audit` (log only) |
| `trusted_domains` | Allow internal services whose public DNS resolves to private IPs |
| `forward_proxy` | Outbound HTTPS scanning (tunnel timeouts, idle timeouts) |
| `dlp` | Data loss prevention (scan env vars, built-in patterns) |
| `response_scanning` | Scan LLM responses for prompt injection |
| `mcp_input_scanning` | Scan inbound MCP requests |
| `mcp_tool_scanning` | Validate tool calls, detect drift |
| `mcp_tool_policy` | Pre-execution rules, shell obfuscation, redirect profiles |
| `mcp_session_binding` | Pin tool inventory, detect manipulation |
| `tool_chain_detection` | Multi-step attack patterns |
| `websocket_proxy` | WebSocket frame scanning (disabled by default) |
| `logging` | Output format (json/text), verbosity |
For the Helm chart, most sections are configurable via `values.yaml`. For additional sections not covered by structured values (session profiling, data budgets, kill switch, sandbox, reverse proxy, adaptive enforcement), use the `extraConfig` escape hatch:
```yaml
pipelock:
extraConfig:
session_profiling:
enabled: true
max_sessions: 1000
```
## Modes
| Mode | Behavior | Use case |
|------|----------|----------|
| `strict` | Block all detected threats | Production with sensitive data |
| `balanced` | Warn on low-severity, block on high-severity | Default; good for most deployments |
| `audit` | Log everything, block nothing | Initial rollout, testing |
Start with `audit` mode to see what Pipelock detects without blocking anything. Review the logs, then switch to `balanced` or `strict`.
## Limitations
- Forward proxy only covers Faraday-based HTTP clients. Net::HTTP, HTTParty, and other libraries ignore `HTTPS_PROXY`.
- Docker Compose has no egress network policies. The `/mcp` endpoint on port 3000 is still reachable directly (auth token required). For enforcement, use Kubernetes NetworkPolicies.
- Pipelock scans text content. Binary payloads (images, file uploads) are passed through by default.
## Troubleshooting
### Pipelock container not starting
Check the config file is mounted correctly:
```bash
docker compose -f compose.ai.yml logs pipelock
```
Common issues:
- Missing `pipelock.example.yaml` file
- YAML syntax errors in config
- Port conflicts (8888 or 8889 already in use)
### LLM calls failing with proxy errors
If AI chat stops working after enabling Pipelock:
```bash
# Check Pipelock logs for blocked requests
docker compose -f compose.ai.yml logs pipelock --tail=50
```
If requests are being incorrectly blocked, switch to `audit` mode in the config file and restart:
```yaml
mode: audit
```
### MCP requests not reaching Sure
Verify the MCP upstream is configured correctly:
```bash
# Test from inside the Pipelock container
docker compose -f compose.ai.yml exec pipelock /pipelock healthcheck --addr 127.0.0.1:8888
```
Check that `MCP_API_TOKEN` and `MCP_USER_EMAIL` are set in your `.env` file and that the email matches an existing Sure user.