queryshield

QueryShield

Agent security suite. QueryShield is the lead product: a secure SQL proxy and a named credential vault, one qs_ API key, one Stripe customer, and this docs site.

Agents call a single endpoint in plain English (or structured SQL). QueryShield:

1. Translates natural language → SQL via Claude with prompt caching.
2. Validates every query at the AST level — only SELECT is allowed, no stacked statements, no forbidden functions, LIMIT required.
3. Applies per-agent row-level security: schema/table whitelists and WHERE clause injection.
4. Executes against the customer DB and returns rows.
5. Logs every attempt to an append-only audit table — metadata only, never row contents.

Agents never see connection strings.

The credential vault stores other secrets (API tokens the agent is allowed to use). It is the AgentVault product, folded into this service. Permission patterns decide which names an agent can read. Database connection strings stay in a separate encrypted store and are not readable through the vault.

Free tier / funnel: InjectShield scans untrusted text for prompt injection. It stays in its own repository (bch1212/injectshield) and its own API (api.injectshield.dev). Start there, then create one QueryShield workspace when an agent needs a database or a vault. Docs: injectshield.dev/docs. Suite page on this service: /suite.

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Quickstart

pip install -r requirements.txt
cp .env.example .env
# Set ANTHROPIC_API_KEY, DATABASE_URL, and VAULT_KEY in .env (see below).
# Do not commit .env. QueryShield reads secrets from the environment only.

python -m queryshield.start

Generate a Fernet key for VAULT_KEY once and never lose it. Credential encryption and session signing both require VAULT_KEY (or SESSION_KEY for cookies). If it is unset, those operations fail instead of using a built-in key:

python -c "from cryptography.fernet import Fernet; print(Fernet.generate_key().decode())"

---

End-to-end flow (curl)

# 1) Boot a tenant. Returns the admin API key — copy it.
curl -X POST localhost:8000/v1/tenants?name=Acme

# 2) Register the customer DB. Connection string is encrypted at rest.
curl -X POST localhost:8000/v1/databases \
  -H 'X-Admin-Key: qs_...' \
  -H 'Content-Type: application/json' \
  -d '{
    "alias": "prod",
    "db_type": "postgresql",
    "connection_string": "postgresql://READER:[email protected]:5432/app",
    "allowed_tables": ["users", "orders"]
  }'

# 3) Provision a scoped agent (different from admin) for your AI app.
curl -X POST localhost:8000/v1/agents \
  -H 'X-Admin-Key: qs_...' \
  -H 'Content-Type: application/json' \
  -d '{ "name": "reporting", "tenant_id": "<tenant>" }'

# 4) Set the agent's RLS policy.
curl -X POST localhost:8000/v1/policies \
  -H 'X-Admin-Key: qs_...' \
  -H 'Content-Type: application/json' \
  -d '{
    "agent_id": "<agent>",
    "database_alias": "prod",
    "allowed_tables": ["users", "orders"],
    "row_filters": { "users": "tenant_id = 42" }
  }'

# 4b) Store a named credential and let one agent read it.
#     The value is write-only on this call. Empty permissions grant nothing.
curl -X POST localhost:8000/v1/credentials \
  -H 'X-Admin-Key: qs_...' \
  -H 'Content-Type: application/json' \
  -d '{
    "name": "openai_key",
    "provider": "openai",
    "value": "YOUR_SECRET"
  }'

curl -X POST localhost:8000/v1/agents \
  -H 'X-Admin-Key: qs_...' \
  -H 'Content-Type: application/json' \
  -d '{
    "name": "writer",
    "tenant_id": "<tenant>",
    "credential_permissions": ["openai_*"],
    "budget_daily": 5
  }'

# The agent uses the same qs_ key it uses for /v1/query.
curl -X POST localhost:8000/v1/vault/get/openai_key \
  -H 'X-API-Key: qs_...'

# 5) The agent queries.
curl -X POST localhost:8000/v1/query \
  -H 'X-API-Key: qs_...' \
  -H 'Content-Type: application/json' \
  -d '{
    "database_alias": "prod",
    "query": "how many active users do we have?",
    "mode": "nl",
    "max_rows": 10
  }'

---

MCP integration

Listed in the official MCP Registry as io.github.bch1212/queryshield.

Install the client:

pip install queryshield-mcp

Then drop this into your Claude Desktop / Cursor / agent config:

{
  "queryshield": {
    "command": "queryshield-mcp",
    "env": { "QUERYSHIELD_API_KEY": "qs_..." }
  }
}

Source for the standalone PyPI package lives in packages/queryshield-mcp/.

For MCP directory evaluators such as Glama, the repository root also includes a slim Dockerfile that launches the published queryshield-mcp stdio server for tool introspection. The container does not need QUERYSHIELD_API_KEY for MCP initialization/tool discovery; the key is only required when a discovered tool is actually invoked against a QueryShield API tenant.

MCP integration (legacy)

Drop this into any MCP-aware client (Claude Desktop, Cursor, custom agents):

{
  "queryshield": {
    "command": "python",
    "args": ["-m", "queryshield.mcp_server"],
    "env": {
      "QUERYSHIELD_API_KEY": "qs_...",
      "QUERYSHIELD_BASE_URL": "https://api.queryshield.io"
    }
  }
}

Tools exposed:

• query_database(database_alias, question, max_rows) — natural-language
• query_database_sql(database_alias, sql, max_rows) — pre-built SELECT
• list_credentials() — names this agent may read
• get_credential(name, cost) — one permitted secret, same API key
• get_audit_log(limit) — recent attempts for the calling agent

The PyPI package queryshield-mcp picks up the vault tools on its next release. Until then, python -m queryshield.mcp_server from this repo already exposes them.

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Security model

Threat	Defense
Agent crafts a DROP TABLE	sqlglot AST refuses non-SELECT
Agent sneaks ; and a second statement	parser rejects len(statements) > 1
Agent uses pg_sleep, xp_cmdshell, ...	function deny-list at the AST node level
Agent reads tables outside its scope	RLS schema + table whitelist
Agent reads other tenants' rows	row_filters injected via AST .where()
Connection string leaks via stack traces	Fernet-encrypted, never returned in any API
Agent reads a secret it was not granted	fnmatch permissions; empty list fails closed
Vault audit becomes the secret	credential access log stores metadata only
Audit log becomes the data exfil vector	only metadata is stored — never rows
VAULT_KEY rotation	re-encrypt rows under new key (script-driven)

safety.py is the single most important module. Every additional check that lands there should ship with a test in tests/test_safety.py.

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Pricing

Tier	Monthly	Databases	Credentials	Queries / month	Notes
InjectShield	free	—	—	—	prompt-injection funnel, separate site
Starter	$500	3	25	1,000,000
Pro	$1,500	10	100	10,000,000	audit export
Enterprise	$3,500	unlimited	unlimited	unlimited	SSO, SIEM webhook

QueryShield tiers are one Stripe customer. InjectShield sign-up stays on injectshield.dev until that billing is folded in. Targets $32.5K MRR @ 15 customers (10 Pro + 5 Enterprise).

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Deploy

The repo is Railway-ready. python -m queryshield.start is the entrypoint (reads PORT via os.getenv, since Railway exec's the start command without a shell). Provision Postgres + (optionally) Redis from Railway's marketplace and the rest is env vars.

railway up

/health is the liveness check. /ready returns 503 if the control-plane DB is unreachable.

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Tests

pip install pytest
python -m pytest tests/

102 tests cover the original safety, RLS, proxy, and HTTP suites plus the credential vault (encryption, qs_ key hashing, permission checks, budgets, tenant isolation, and the column migration for existing databases) and fail-closed VAULT_KEY / SESSION_KEY checks. The original cases:

• AST safety (24 cases — direct DDL, comments, encoded keywords, multiple statements, forbidden functions, missing LIMIT)
• RLS engine (6 cases — whitelist enforcement, WHERE injection, conjunction with existing predicates)
• Proxy end-to-end against a SQLite "customer DB" (5 cases — happy path, blocked DML, RLS row filtering, table whitelist, cache hit)
• HTTP integration via FastAPI TestClient (7 cases — full provisioning → query flow, scoped agent with RLS, auth failures)