Airlock
A redaction engine and MCP server that scrubs sensitive identifiers (AWS keys, IPs, emails, etc.) from AI agent traces, enabling safe storage and sharing of war stories via submit, search, and retrieve tools.
README
Airlock
Redaction engine + MCP server for AI agent traces. By AgentPier.
Airlock is the missing seam between your secret scanner and your PII redactor. It catches the identifiers that neither tool covers alone: AWS account IDs, ARNs, private and public IPs, hostnames, emails, access keys, bearer tokens, and high-entropy secrets — all in one pass, all replaced with typed placeholders so the lesson survives and the identifier doesn't.
Built to protect war stories: battle-tested lessons from real agent sessions, published without the infra details that would make them a reconnaissance target.
Quickstart
pip install agentpier-airlock
python -c "from airlock.scrubber import scrub; print(scrub('account 123456789012 at 10.0.0.5'))"
# → account <ACCOUNT_ID> at <PRIVATE_IP>
Or run the demo:
git clone https://github.com/gatewaybuddy/agentpier.git
cd agentpier
python examples/demo.py
Demo output
========================================================================
AIRLOCK — Redaction Demo
========================================================================
--- BEFORE (raw agent trace) -------------------------------------------
[2026-06-24T14:32:01Z] AgentRun#7f3a2c1e — inventory task started
Caller identity: arn:aws:iam::123456789012:user/deploy-agent
Account: 123456789012 Region: us-east-1
Probing EC2 in us-east-1...
→ Instance i-0abc1234def56789 at 203.0.113.42 (public), 10.0.1.55 (private)
→ Security group sg-0123456789abcdef0 allows 0.0.0.0/0:443
S3 buckets found:
s3://example-prod-data/logs/2026-06/
s3://example-backups/snapshots/
Cross-account replication target: --bucket-name example-dr-replica
Secrets Manager: arn:aws:secretsmanager:us-east-1:123456789012:secret:prod/db-creds-xK8mP2
Retrieved value: {"password": "s3cr3tP@ssw0rd!", "host": "db.internal.example.net"}
Lambda env vars on arn:aws:lambda:us-east-1:123456789012:function:data-processor:
AWS_ACCESS_KEY_ID=AKIAIOSFODNN7EXAMPLE
AWS_SECRET_ACCESS_KEY=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
Notification endpoint: ops-alerts@example.com
On-call SMS: 407-555-0192
Internal dashboard: https://monitoring.internal.example.net/dashboard
[2026-06-24T14:32:04Z] Inventory complete — 2 instances, 3 buckets, 1 function
--- AFTER (scrubbed) ---------------------------------------------------
[2026-06-24T14:32:01Z] AgentRun#7f3a2c1e — inventory task started
Caller identity: <ARN>
Account: <ACCOUNT_ID> Region: us-east-1
Probing EC2 in us-east-1...
→ Instance i-0abc1234def56789 at <PUBLIC_IP> (public), <PRIVATE_IP> (private)
→ Security group sg-0123456789abcdef0 allows <PUBLIC_IP>/0:443
S3 buckets found:
<BUCKET><PATH>
<BUCKET><PATH>
Cross-account replication target: --<BUCKET>
Secrets Manager: <ARN>
Retrieved value: {"password": "s3cr3tP@ssw0rd!", "host": "<FQDN>"}
Lambda env vars on <ARN>
AWS_ACCESS_KEY_ID=<ACCESS_KEY>
AWS_SECRET_ACCESS_KEY=<SECRET>
Notification endpoint: <EMAIL>
On-call SMS: <PHONE>
Internal dashboard: https://<FQDN>/dashboard
[2026-06-24T14:32:04Z] Inventory complete — 2 instances, 3 buckets, 1 function
--- GATE RESULT --------------------------------------------------------
clean: False status: DIRTY — quarantined
findings: 22
========================================================================
22 sensitive tokens redacted. The lesson survives; the identifiers don't.
========================================================================
What it catches
| Category | Examples | Placeholder |
|---|---|---|
| AWS access keys | AKIA... |
<ACCESS_KEY> |
| Private key blocks | -----BEGIN RSA PRIVATE KEY----- |
<SECRET> |
| Bearer tokens / passwords | Authorization: Bearer ... |
<SECRET> |
| AWS account IDs | 12-digit numeric IDs | <ACCOUNT_ID> |
| ARNs | arn:aws:... |
<ARN> |
| S3 bucket names (in URIs) | s3://my-bucket/ |
<BUCKET> |
| Private IPs | RFC 1918 (10.x, 172.16–31.x, 192.168.x) | <PRIVATE_IP> |
| Public IPs | Routable IPv4 | <PUBLIC_IP> |
| Hostnames / FQDNs | api.example.com |
<FQDN> |
| Email addresses | user@example.com |
<EMAIL> |
| US phone numbers | 407-555-0192 |
<PHONE> |
| Absolute paths | /home/user/.ssh/id_rsa |
<PATH> |
| High-entropy strings | Tokens, UUIDs-as-secrets (Shannon > 4.0 bits/char) | flagged by gate |
| Your infra names | Bucket/host names you configure | <BUCKET> |
Usage
Scrub a string
from airlock.scrubber import scrub, gate
text = "Deployed to account 123456789012, endpoint api.example.com"
# scrub() — always redacts, returns clean string
print(scrub(text))
# → "Deployed to account <ACCOUNT_ID>, endpoint <FQDN>"
# gate() — DEFAULT-DENY: returns clean=False on ANY finding
result = gate(text)
print(result["clean"]) # False
print(result["findings"]) # [{rule: "AWS_ACCOUNT_ID", ...}, ...]
Add your org's infra names to the denylist
from airlock.scrubber import add_to_denylist
add_to_denylist(["my-prod-bucket", "internal.corp.net"])
Or via environment variable (for MCP server deployments):
export AIRLOCK_BUCKET_DENYLIST="my-prod-bucket,internal.corp.net,staging-data"
airlock-mcp
Start the MCP server
# As a console script (after pip install):
AIRLOCK_BUCKET_DENYLIST="my-bucket" airlock-mcp
# Or directly from the repo:
PYTHONPATH=. python airlock/server.py
See airlock/SKILL.md for Claude Code / Cursor install config.
Safety design
DEFAULT-DENY gate: gate() returns clean=False if anything sensitive
is found. There is no partial-clean state. A story either passes the gate
completely or it is quarantined.
Double-gating:
- Gate on ingest — story is rejected if dirty; not stored
- Scrub on egress — every return path runs
scrub()(defense-in-depth)
Denylist is empty by default: Airlock ships with zero hardcoded infra names.
You bring your own via AIRLOCK_BUCKET_DENYLIST or add_to_denylist(). This
keeps the library from embedding any org's topology.
MCP tools
| Tool | Description |
|---|---|
submit_story |
Submit a war story. Gate-on-ingest: rejected with findings if dirty. |
search_stories |
Keyword + tag search. All results scrubbed on egress. |
get_story |
Fetch story by UUID. Scrubbed on egress. |
See airlock/SKILL.md for the full install guide and MCP config snippet.
Schema
Stories follow the AgentErrorTaxonomy. Required fields: id, title,
situation, goal, what_i_tried, what_failed, what_worked, lesson,
tags, narrator_id, timestamp, trust. At least one taxonomy tag is
required: memory | reflection | planning | action | system.
See airlock/STORY_TEMPLATE.md for the fill-in-the-blanks template.
Running tests
# From the repo root
PYTHONPATH=. python -m pytest airlock/tests/ -v
The test suite includes unit tests for the scrubber (41 tests), handler tests (18 tests), and end-to-end subprocess smoke tests speaking real JSON-RPC 2.0 wire protocol (10 tests). All fixtures use RFC-reserved / AWS-documented identifiers — no real infrastructure values.
License
Apache 2.0. See LICENSE.
Contributing
See CONTRIBUTING.md.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.