ZTAgent

Zero Trust Security for AI Agents

Never trust an agent action. Verify before execution.

ZTAgent is an open-source security framework for AI agents, from small developer projects to enterprise systems. Originally developed by Victor Fang in 2026.

• Product: https://ztagent.ai
• Open source: https://github.com/victorfang/ztagent
• Contact the ZTAgent.ai team: https://forms.gle/Bq4XNxcSVD2aHrrK6
• Author: VictorFang.com · X · LinkedIn

This repository is the Apache-2.0 ZTAgent Core gateway at the center of ZTAgent. The PyPI package is ztagent; the import remains ztagent_core. It puts one enforcement pipeline in front of model and tool calls, with useful secure defaults, without replacing your identity provider, reverse proxy, model host, or SIEM.

pip install ztagent
ztagent --help

Editions and services

Use Core when you want a compact, inspectable security boundary. Enterprise is planned for teams that need vendor-supported controls beyond the open-source core. Until that ships, ztagent.ai offers consulting to customize the gateway for your identity, policy, and tool landscape.

Contact the ZTAgent.ai team: https://forms.gle/Bq4XNxcSVD2aHrrK6

This is a security foundation, not a claim that signature matching makes an AI system completely safe. Prompt injection is not a solved problem. Layer the gateway with least-privilege tools, sandboxing, egress controls, human approval for consequential actions, and application-specific evaluation.

Where ZTAgent sits in the AI agent stack

ZTAgent is the policy enforcement point between your orchestrator (LangGraph, LangChain, or a custom loop) and every model or tool call. It does not replace the identity provider, reverse proxy, model host, or SIEM.

flowchart TB
  U[User / client]
  APP[Your app / API]
  IDP[Identity<br/>Auth0 / Keycloak / OIDC]
  ORCH[Orchestrator<br/>LangGraph / LangChain / custom]
  ZT["ZTAgent Core<br/>verify before execution"]
  M[Models<br/>OpenAI / Anthropic / compatible]
  T[Registered tools]
  OPA[OPA policy]
  AUD[Audit / portal / SIEM export]

  U --> APP
  APP --> IDP
  IDP --> ORCH
  ORCH -->|"every model and tool call"| ZT
  ZT --> M
  ZT --> T
  ZT --> OPA
  ZT --> AUD

Inside ZTAgent the request is verified, then allowed or blocked:

flowchart LR
  IN[Model or tool request] --> JWT[OIDC JWT]
  JWT --> BL[Containment blocklist]
  BL --> SIG[Signature scan]
  SIG --> ANO[Anomaly counters]
  ANO --> POL[OPA allow / deny]
  POL -->|allowed| EXEC[Execute model or tool]
  POL -->|denied| DENY[Block + audit]
  EXEC --> OUT[Scan tool output]
  OUT --> AUD2[HMAC-chained audit]

Bypass is a missed edge: if LangGraph still calls OpenAI or a webhook directly, ZTAgent never sees the action.

Detect, contain, and revoke a malicious agent

A stolen or abused Auth0 (or Keycloak) token can still pass JWT checks. ZTAgent does not trust that token for execution. When a contain detection fires — destructive-command signatures, or enough blocked events in 24 hours — it stops the current call, writes the JWT sub to a local blocklist, then asks identity to kill sessions. Local deny is applied first, so an Auth0 or Keycloak outage cannot restore access.

sequenceDiagram
  autonumber
  actor Agent as Malicious agent
  participant ZT as ZTAgent Core
  participant BL as Local identity blocklist
  participant AUD as HMAC audit
  participant IdP as Auth0 / Keycloak / OIDC
  participant WH as Containment webhook

  Agent->>ZT: Model or tool call with a still-valid JWT
  ZT->>ZT: Verify issuer, audience, signature, expiry
  ZT->>ZT: Signatures, anomaly counters, OPA
  Note over ZT: contain signature or repeated blocks in 24h
  ZT-->>Agent: 403 block this request
  ZT->>BL: Contain subject immediately
  ZT->>AUD: security_detection contained true
  opt Keycloak admin configured
    ZT->>IdP: Logout all sessions for that user
  end
  opt webhook configured
    ZT->>WH: subject, reason, event_id
    WH->>IdP: Revoke Auth0 sessions or refresh tokens
  end
  Agent->>ZT: Later retry with the same JWT
  ZT->>BL: Subject already contained?
  ZT-->>Agent: 403 Identity is contained

Core Open Source implements the local blocklist and Keycloak session logout. Auth0 (and other IdPs) are revoked through containment.webhook_url — point that at a small Automation/SOAR job that calls the Auth0 Management API. The gateway remains closed even if that webhook fails. An administrator unblocks a subject through the protected admin API after review.

Three ways to use ZTAgent

Pick one path. You can start with 1 and move to 2 or 3 later; the same signatures, tool registry, and gateway code sit underneath.

Hands-on attack examples (injection, malicious tools) are in the tutorial. Architecture detail is in docs/architecture.md.

1. Standalone ZTAgent

Use this for a new project or a demo. ZTAgent owns the HTTP API, model call, optional tools, audit log, and portal. Development defaults leave authentication off and allow running without OPA.

python -m venv .venv
source .venv/bin/activate
pip install -e .
cp .env.example .env
# OPENAI_API_KEY=...
# ZTAGENT_AUDIT_HMAC_KEY=$(ztagent secret)
ztagent check
ztagent serve

Open http://127.0.0.1:8000/admin. Call /v1/agent/run with a user message. You do not need Auth0, LangGraph, or Docker for this path. Turn on OIDC and fail-closed OPA before production (server.environment: production).

ztagent init my-agent scaffolds the same files in a new directory.

2. Integrate LangGraph, Auth0, and your app

Keep your identity provider and graph. Insert ZTAgent as the only path to models and side-effecting tools so the graph cannot bypass policy.

1. Point auth at Auth0 (or Keycloak): issuer, audience, jwks_url, algorithms: [RS256], and role_claim matching your token (permissions, realm_access.roles, or a custom namespaced claim).
2. Verify the access token, then build Principal from that result — never from model output or LangGraph state.
3. Put as_langchain_runnable(gateway, principal) on the model node. Send high-risk tools through gateway.execute_tool(...), not raw HTTP/SDK calls.

pip install 'ztagent[langchain]'
# From this repository: pip install -e '.[langchain]'

from ztagent_core.api import create_gateway
from ztagent_core.auth import JWTAuthenticator
from ztagent_core.config import load_config
from ztagent_core.integrations import as_langchain_runnable

config = load_config()
gateway = create_gateway(config, tools=registry)
principal = JWTAuthenticator(config.auth).verify(access_token)  # Auth0 JWT
secure_node = as_langchain_runnable(gateway, principal)

# LangGraph: attach secure_node as the model edge.
result = await secure_node.ainvoke({"messages": [{"role": "user", "content": "Hello"}]})

Auth0 example config/agent.yaml fragment:

auth:
  enabled: true
  issuer: https://YOUR_TENANT.auth0.com/
  audience: ztagent-core
  jwks_url: https://YOUR_TENANT.auth0.com/.well-known/jwks.json
  algorithms: [RS256]
  role_claim: permissions

The same pattern works with Keycloak or any OIDC provider that issues RS256 access tokens. See the LangGraph + Auth0 section.

3. Import only the modules you need

Use this when you already have an API and only want selected controls — for example prompt-injection scanning — without running ztagent serve.

from pathlib import Path
from ztagent_core.guardrails import SignatureScanner
from ztagent_core.tools import ToolRegistry, ToolSpec

scanner = SignatureScanner.from_file(Path("config/signatures.yaml"))
findings = scanner.scan(user_prompt)
if any(item.action in {"block", "contain"} for item in findings):
    raise PermissionError("Request blocked by ZTAgent signatures")

Other drop-in pieces: ToolRegistry / ToolSpec (schema-validated tools), SecureAgentGateway (full PEP without FastAPI), JWTAuthenticator, AuditLog, AnomalyDetector, ContainmentService, OPAClient. You are responsible for calling them on every model and tool path; a missed edge is a bypass.

What is included

• FastAPI-based API gateway with request limits and security headers
• OIDC JWT validation for Keycloak, Auth0, and compatible identity providers
• OPA policy enforcement point/client with fail-closed production defaults
• Registered, schema-validated tool gateway with risk metadata
• YAML signature rules with Unicode normalization and regex timeouts
• OpenAI Responses, Anthropic Messages, and OpenAI-compatible model APIs
• Optional LangChain Runnable adapter; the core remains orchestrator-neutral
• Stateless request heuristics and memory/SQLite rolling 24-hour counters
• HMAC-chained JSONL audit records with default secret/prompt redaction
• Local identity containment, Keycloak session logout, and a webhook for Auth0 or SOAR revocation
• Protected, dependency-free web administration portal
• Friendly project wizard and operational checks

Quick start (standalone)

Requires Python 3.11+. For the standalone path you only need an OpenAI (or compatible) API key and an audit HMAC key. Docker/OPA is optional until you disable the development policy bypass.

python -m venv .venv
source .venv/bin/activate
pip install ztagent
# From this repository, developers can instead: pip install -e .

# Create a separate starter project, or use this repository's included example.
ztagent init my-agent
cd my-agent
cp .env.example .env
# OPENAI_API_KEY=sk-...
ztagent secret                         # put output in ZTAGENT_AUDIT_HMAC_KEY
set -a; source .env; set +a
ztagent check
# Optional until you disable the OPA development bypass:
# docker compose up -d
ztagent serve

Open http://127.0.0.1:8000/admin. Development defaults disable authentication; the portal therefore uses a development administrator. Production configuration validation refuses disabled authentication or an OPA bypass.

Call the gateway (no bearer token required in standalone development):

curl http://127.0.0.1:8000/v1/agent/run \
  -H 'Content-Type: application/json' \
  -d '{"messages":[{"role":"user","content":"Summarize this report"}]}'

Provider setup

Set provider.kind in config/agent.yaml:

Models are deny-by-default: a requested model must appear in allowed_models. API keys are read only from environment variables and are never returned or written to audit events.

Identity and policy

On the standalone path, leave auth.enabled: false only in development. On the integrate path, configure the issuer, audience, JWKS URL, fixed asymmetric algorithms, and role claim under auth for Auth0, Keycloak, or another OIDC provider. ZTAgent verifies signature, expiration, issued-at, issuer, audience, and subject. Do not derive accepted JWT algorithms from a token.

The gateway is the policy enforcement point (PEP). It sends only identity, roles, action, resource metadata, source IP, and request ID to OPA (the PDP). Prompts and tool arguments are not sent to OPA. The starter Rego policy allows:

• authenticated model calls;
• low/medium-risk tools for authenticated identities;
• high-risk tools only for the ztagent-tool-admin role.

Tailor policies/authz.rego to tenant, model, tool, data classification, and business approval requirements. OPA is bound to loopback in compose.yaml.

Add a tool

from pydantic import BaseModel
from ztagent_core.tools import ToolRegistry, ToolSpec


class LookupArgs(BaseModel):
    ticket_id: str


registry = ToolRegistry()
registry.register(
    ToolSpec(
        name="lookup_ticket",
        description="Read one support ticket",
        arguments=LookupArgs,
        handler=lambda args: {"id": args.ticket_id},
        risk="low",
    )
)

Pass the registry to create_gateway(config, registry). For standalone, also pass it to create_app(config, gateway). For LangGraph, keep the same registry on the gateway the graph calls. Every /v1/tools/{name} request (and gateway.execute_tool) receives authentication, signature scanning, OPA authorization, validation, and audit. The registry intentionally has no shell, filesystem, or arbitrary HTTP tool.

LangChain / LangGraph

The optional adapter is a LangChain Runnable, so it works as a LangGraph node. See path 2 above. The application must derive principal from a verified request; never accept identity or roles from model output or untrusted chain state.

Before/after demo agents

Install the demo extra and run three small LangChain applications:

pip install 'ztagent[demos]'
# From this repository: pip install -e '.[demos]'
ztagent demo stock-injection
ztagent demo unauthorized-publish
ztagent demo article-only

Each command contrasts a deliberately vulnerable baseline with the protected framework path. The examples cover an indirect prompt injection hidden in stock data, unauthorized article publishing, and a safe article-only workflow. Email, DM, and social actions always remain in a local JSONL sandbox.

Use --live to exercise the configured OpenAI API or stay with the default deterministic offline model for a repeatable, credential-free security demo. See the tutorial for a step-by-step walkthrough of configuration, prompt injection, and malicious tool use. See docs/demos.md for expected output, the exact controls being demonstrated, and important limitations.

CLI

ztagent init [DIRECTORY]   Generate config, signatures, Rego, Compose, and app files
ztagent secret             Generate a strong audit HMAC key
ztagent check              Validate configuration and signatures; flag dev bypasses
ztagent demo               Compare vulnerable and secured LangChain demo agents
ztagent serve              Start the gateway and portal

Architecture and security scope

See the hands-on tutorial for configuration, prompt-injection blocks, malicious tool use, and audit examples. See docs/architecture.md for the request flow, threat coverage, design decisions, limitations, deployment checklist, and research references. See SECURITY.md for vulnerability reporting and operational security guidance. The blast-radius threat-modeling tutorial provides worked examples for the included agents.

Development

pip install -e '.[dev]'
ruff check .
mypy
pytest

Apache-2.0 licensed. Product: ZTAgent · source: github.com/victorfang/ztagent.