bastiongate

MCP security gateway. An inline proxy that sits between an AI agent and its MCP servers and enforces security on every call:

• scans tools/list and drops tools whose definitions carry prompt injection or hidden unicode (via bastionsupply)
• enforces a tool allow/deny policy — the agent can only call what you permit
• scans tool-call results and blocks any that carry indirect prompt injection before the agent ever reads them
• logs every message as a JSONL trace for forensics

The runtime-enforcement leg of the bastion family:

Install

pip install bastiongateway

(The PyPI distribution is bastiongateway; the import package and bastiongate CLI keep that name.)

Use

The gate is an MCP server to your agent, and a client to the real one. Point your MCP client's command at the gate and put the real server after --:

// mcp.json
{
  "mcpServers": {
    "docs": {
      "command": "bastiongate",
      "args": ["run", "--policy", "policy.yaml", "--log", "gate.jsonl",
               "--", "npx", "-y", "@some/mcp-server"]
    }
  }
}

Everything the agent sends flows through the gate to the server and back, with the checks applied in between.

Policy

Drop in the same YAML bastionsupply harden emits:

default: deny
allow:
  - get_weather
  - search_docs
deny:
  - run_command
# behavior knobs (defaults shown)
scan_tools: true            # scan tools/list
on_poisoned_tool: block     # drop poisoned tools from the listing
scan_results: true          # scan tool-call results
on_injected_result: block   # block results carrying injection
scrub_args: true            # scan tool-call arguments for secrets/PII
on_pii_arg: redact          # redact | block | warn
scrub_results: false        # scan tool-call RESULTS for secrets/PII (opt-in)
on_pii_result: redact       # redact | block | warn
result_inspector: static    # static | agentbastion  (deeper inspection)
inspector_fail: closed      # closed | open  (behavior if the inspector errors)
inspector_judge: false      # agentbastion: also use the Anthropic LLM judge
inspector_semantic: false   # agentbastion: also use the semantic detector

# per-tool overrides — any of the knobs above, scoped to one tool
tools:
  send_email:
    scrub_args: false       # the recipient email is the point; don't redact it
  fetch:
    on_injected_result: warn

So the pipeline is: scan the server with bastionsupply → harden a policy → run it live behind bastiongate.

Argument PII/secret scrub

On every tools/call the gate scans the arguments the agent is about to send and, by default, redacts secrets/PII ([REDACTED:<kind>]) before they reach the tool — API keys, AWS/GitHub/Slack tokens, private keys, JWTs, emails, SSNs, and Luhn-valid card numbers. on_pii_arg: block refuses the call instead; warn only logs. Only the kind is ever logged, never the value.

HTTP transport

For MCP Streamable-HTTP servers, run the gate as an HTTP proxy instead:

bastiongate run-http --upstream http://127.0.0.1:8000/mcp --port 9000 --policy policy.yaml

Point your client at http://127.0.0.1:9000/mcp. JSON and SSE responses are both gated. Binds 127.0.0.1 by default. Add --auth-key KEY (or env BASTIONGATE_PROXY_KEY) to require an X-Bastiongate-Key header on every request; the key is compared in constant time and never forwarded upstream.

Deeper result inspection (agentbastion)

result_inspector: agentbastion swaps the static signature scan for agentbastion's inbound Firewall, composed of up to three tiers: heuristics (always), the semantic detector (inspector_semantic: true + BASTIONGATE_EMBED_URL), and the Anthropic LLM judge (inspector_judge: true + ANTHROPIC_API_KEY).

pip install "bastiongateway[agentbastion]"           # heuristic
pip install "bastiongateway[agentbastion-local]"     # + semantic (local model, no egress)
pip install "bastiongateway[agentbastion-semantic]"  # + semantic (remote embed endpoint)
pip install "bastiongateway[agentbastion-judge]"     # + LLM judge

The semantic detector needs an embedder, chosen by env:

• BASTIONGATE_EMBED_MODEL — a local sentence-transformers model (e.g. all-MiniLM-L6-v2). Result text never leaves the process. Preferred.
• BASTIONGATE_EMBED_URL — a self-hosted embeddings endpoint (result text is POSTed to it).

Try it

bastiongate run --log gate.jsonl -- python examples/echo_server.py

The example server offers a poisoned tool and an injected result; the gate drops the first and blocks the second. Watch gate.jsonl.

Library

from bastiongate import Gate, GatePolicy

gate = Gate(GatePolicy(deny={"run_command"}))
forward, reply = gate.handle_client_msg(msg)   # agent -> server
out = gate.handle_server_msg(response)          # server -> agent

Gate is a pure message transform — easy to embed or test.

Security notes & limitations

• Argument redaction can alter legitimate calls. on_pii_arg: redact rewrites anything that looks like PII — including a recipient email a send_email tool actually needs. Scope it with a per-tool scrub_args: false or on_pii_arg: warn (see tools: above). Scrubbing is best-effort DLP: base64-encoded or field-split secrets can slip through.
• Result scrub covers both text content blocks and structuredContent.
• The HTTP listener throttles an IP after repeated auth failures (429), and correlation state is bounded per session with an idle TTL.
• The HTTP proxy adds no auth of its own. It binds 127.0.0.1 by default and passes the client's Authorization header through to the upstream. Do not bind a public interface without an auth layer in front.
• The HTTP proxy does not follow upstream redirects and only speaks http/https — a malicious upstream cannot bounce it to file:// or an internal address.
• JSON-RPC batch arrays on the request side are passed through un-gated (responses are still scanned); single messages — the normal case — are fully gated. Chunked request bodies (no Content-Length) are not supported.
• inspector_fail: closed (default) blocks a result if the inspector errors or times out. The LLM judge runs on every result (cost + latency); it has a BASTIONGATE_JUDGE_TIMEOUT (default 10s).

MIT.