GitHub - Elliot-Sones/AgentGate: AgentGate scans AI agents before deployment, catching threats from both sides (malicious agent behavior and adversarial users) by sandboxing them, comparing actual behavior against declared behavior, and returning a trust verdict of allow or block.

Trust and verification engine for AI agent marketplaces.
One API call to scan any agent. Real-time results. Typed verdicts.


Quick Start	Try it in 30 seconds — one curl command
Results	14 agents tested, 150+ findings, 100% malware detection
How It Works	5-stage pipeline: source review → deploy → attack → adapt → verdict
Sandbox Infrastructure	What every agent gets: LLM keys, Slack/Shopify sandboxes, databases, auto-detected env vars
Security Checks	12 detectors, 6 adaptive specialists, 5 static analyzers
Deployment & Configuration	Self-hosting, env vars, Railway pool, CLI, CI/CD
Reference	Architecture, API features, known limitations, docs

Quick Start

1. Submit a scan

curl -X POST https://agentgate-production-feed.up.railway.app/v1/scans \
  -H "X-API-Key: agk_live_JT5QEVuK.fvUb2AEGiD5VaW8caIdrFlN7ZGgHuNUO" \
  -H "Content-Type: application/json" \
  -d '{"repo_url": "https://github.com/owner/agent"}'

That's it. One call. AgentGate clones the repo, analyzes the source, deploys the agent to a sandbox, runs 12 security detectors against it, and returns a verdict.

2. Watch it in real-time

curl -H "X-API-Key: agk_live_JT5QEVuK.fvUb2AEGiD5VaW8caIdrFlN7ZGgHuNUO" \
  "https://agentgate-production-feed.up.railway.app/v1/scans/<scan_id>/events?stream=true"

3. Get the report

curl -H "X-API-Key: agk_live_JT5QEVuK.fvUb2AEGiD5VaW8caIdrFlN7ZGgHuNUO" \
  "https://agentgate-production-feed.up.railway.app/v1/scans/<scan_id>/report"

Dashboard

Open dashboard.html in a browser for a visual scan experience with real-time progress, verdict display, and findings breakdown.

What We Found

We tested AgentGate against 14 agents — from popular open-source frameworks to integration-specific fixtures to purpose-built malicious agents.

Agent	What it does	What we found	Verdict
agent-service-toolkit	LangGraph + FastAPI agent framework	Full unified scan: leaked system prompt when asked, provided shell commands for reading /etc/passwd, crashed on XSS payload. 37 checks, 11 failed.	BLOCK
Flowise (47k stars)	No-code chatbot builder	Secretly connecting to outside servers without telling you, and containing phrases that could override agent instructions	BLOCK
MetaGPT (64k stars)	Multi-agent coding framework	Running arbitrary code on your system, executing shell commands, and making hidden internet requests	MANUAL REVIEW
LangChain (105k stars)	LLM application framework	54 findings: dynamic imports via `importlib`, prompt override phrases in test fixtures, outbound HTTP calls, `eval()` usage	MANUAL REVIEW
GPT Researcher (17k stars)	Autonomous research agent	Hidden instruction tokens, dynamic imports, outbound HTTP calls to search APIs, base64 decode usage	MANUAL REVIEW
Lobe Chat (55k stars)	Chat agent platform	Static analysis flagged suspicious code patterns across the Node.js codebase	MANUAL REVIEW
UpMortem/slack-bot	Public Slack bot	Slack sandbox integration exercise: signed event replay, webhook route discovery, bot reply verification	Slack sandbox verified
Slack fixture (`/slack/events`)	Webhook-only Slack agent	Signed event callback accepted, sandbox channel reply observed	Slack sandbox verified
Slack fixture (`/api/slack/events`)	Webhook-only Slack agent (alt route)	Alternate route discovered and exercised via signed event replay	Slack sandbox verified
Shopify fixture (`/shopify/webhooks`)	Webhook-only Shopify agent	Test product created in dev store, signed webhook replayed, agent acknowledged	Shopify sandbox verified
ShopFlow Support	E-commerce customer support	Full unified scan: 37 checks, 34 passed, 3 minor warnings. No malicious behavior, no undeclared network calls.	ALLOW WITH WARNINGS
Trojanized Support Bot	E-commerce customer support	Looks normal, but silently steals your API keys and passwords and sends them to an attacker	CAUGHT
Stealth Exfil Bot	Same support bot, but sneakier	Does the same theft but hides all evidence and only activates when it thinks nobody is watching	CAUGHT
Vulnerable Analytics Agent	Shopify data insights	Hands over customer emails when asked, follows malicious instructions, and makes up fake data	CAUGHT

Results at a glance

14 agents tested across the full unified pipeline
150+ security findings surfaced
14 critical-severity issues in Flowise alone
100% detection rate on intentionally malicious agents
12 security vectors + 6 adaptive specialists per scan
Slack and Shopify sandbox integration verified across 4 agents

How It Works

AgentGate uses staged confidence to verify AI agents before they're listed on a marketplace. Each stage adds evidence. If any stage can't complete, the system classifies why and returns what it has.

Stage	What happens	If it fails
1. Source review	Extracts env vars, framework, auth patterns, probe paths, dependencies, integration surfaces	Always runs
2. Deploy to sandbox	Fills missing config with sandbox values, injects platform credentials, deploys to Railway	`deployment_failed` with explanation
3. Live attack scan	Runs 12 security detectors against the live agent (prompt injection, data exfil, tool misuse, etc.)	`auth_required`, `endpoint_not_found`, `deployment_unusable`, or `boot_timeout`
4. Adaptive runtime review	Specialist agents probe deeper using source review hints and live attack results	Skipped if agent is unresponsive
5. Verdict	Merges all findings, applies trust policy, returns typed verdict	Never crashes — always returns a result

View the interactive architecture diagram on Excalidraw

What the Agent Gets

When AgentGate deploys an agent to the sandbox, it provides real infrastructure so the agent can actually run. Everything is detected automatically from the source code — no manifest required.

Platform Integrations

Integration	What's injected	What happens during the scan
OpenAI	`OPENAI_API_KEY`	Agent can respond to live attack probes via real OpenAI API
Anthropic	`ANTHROPIC_API_KEY`	Agent can respond to live attack probes via real Anthropic API
Slack	`SLACK_BOT_TOKEN`, `SLACK_SIGNING_SECRET`, channel/team IDs	AgentGate replays signed Slack events to the agent's webhook route, then polls the sandbox channel for bot replies. Verification levels: `full` (reply observed), `callback_only` (accepted but no reply), `none` (rejected)
Shopify	`SHOPIFY_ACCESS_TOKEN`, `SHOPIFY_API_SECRET`, `SHOPIFY_STORE_DOMAIN`	AgentGate creates a test product in the dev store, sends a signed `products/create` webhook to the agent, verifies acknowledgment, then deletes the test product

Slack route discovery checks /slack/events, /api/slack/events, /webhooks/slack. Shopify checks /shopify/webhooks, /api/shopify/webhooks, /webhooks/shopify.

All sandbox environments contain only fake data. The agent talks to real APIs but cannot cause real damage.

Backing Services

Provisioned automatically when detected in the agent's dependencies:

Service	How detected	What the agent gets
Postgres	`asyncpg`, `psycopg`, `sqlalchemy`, `DATABASE_URL` in source	Real Postgres instance with `DATABASE_URL` injected
Redis	`redis`, `aioredis`, `REDIS_URL` in source	Real Redis instance with `REDIS_URL` injected
pgvector	`pgvector` in dependencies	Postgres with vector extension enabled
Neo4j	`neo4j`, `NEO4J_URI` in source	Neo4j instance with `NEO4J_URI` injected
Elasticsearch	`elasticsearch` in dependencies	Elasticsearch instance with `ELASTICSEARCH_URL` injected
MySQL	`mysql`, `pymysql` in dependencies	MySQL instance with `MYSQL_HOST` injected
Qdrant	`qdrant-client` in dependencies	Qdrant instance with `QDRANT_URL` injected

Auto-Detected Environment Variables

AgentGate scans the agent's source for os.getenv() and os.environ calls. Any env var the agent needs that isn't already covered gets a safe sandbox value generated automatically:

Secret/key/token/password patterns get random tokens
URL/endpoint patterns get http://localhost:8000
Email patterns get sandbox@agentgate.local
Boolean patterns get true
Everything else gets a random safe string

This means agents that need JWT_SECRET_KEY, SESSION_SECRET, APP_NAME, or other custom config start up without manual configuration.

Security Checks

Static analysis (runs on source code)

Check	What it finds
Code signals	`exec()`, `subprocess`, `importlib`, `socket.connect`, `base64.b64decode`
Prompt/tool inspection	Hidden instructions, prompt overrides, secret exfiltration directives
Dependency risk	Typosquatted packages, missing lockfiles, known malicious deps
Provenance	Unpinned images, missing cosign signatures
Auth detection	FastAPI `Depends`, `@login_required`, `Authorization` header access

Live attack probes (runs against deployed agent)

Detector	What it tests
Prompt injection	DAN jailbreaks, role-play attacks, instruction overrides
System prompt leak	Attempts to extract the system prompt
Data exfiltration	Tries to steal credentials and secrets
Tool misuse	Tests if tools can be invoked outside their scope
Goal hijacking	Attempts to override the agent's objective
XPIA	Cross-prompt instruction attacks via documents
Harmful content	Tests if the agent can be made to produce unsafe or toxic output
Policy violation	Checks adherence to custom policy rules and constraints
Reliability	Tests response consistency and performance under repeated queries
Scope adherence	Verifies the agent stays within its declared purpose and boundaries
Input validation	Tests handling of malformed, oversized, and adversarial inputs
Hallucination	Checks if the agent fabricates facts or cites nonexistent sources

Adaptive trust specialists (LLM-powered deep probes)

Specialist	What it does
Egress prober	Social-engineers the agent into making undeclared network calls
Canary stresser	Seeds fake credentials and checks if they leak (decodes base64, hex, URL encoding, Unicode confusables, char-splitting)
Tool exerciser	Enumerates and exercises all available tool capabilities
Data boundary	Tests cross-tenant and cross-session data isolation
Behavior consistency	Checks if the agent behaves differently across runs
Memory poisoning	Tests if state can be mutated between sessions

Cost per scan

Component	Cost
Security detectors (heuristic, no LLM)	$0.00
Adaptive specialists (10-12 Sonnet calls)	~$0.10
Total	~$0.10

Disable adaptive specialists for $0.00/scan (static + live probes only, zero LLM cost).

Verdicts

Verdict	What happens	When
`ALLOW_CLEAN`	Agent is published automatically	Everything matched its declarations
`ALLOW_WITH_WARNINGS`	Published with notes for the reviewer	Minor issues (e.g. missing dependency lockfile)
`MANUAL_REVIEW`	Sent to a human to decide	Concerning signals (e.g. hidden instructions in prompts)
`BLOCK`	Rejected	Undeclared network connections, stolen credentials, or serious runtime integrity issues detected

When scans can't complete

Failure reason	What it means	What to do
`auth_required`	Agent returned 401/403	Provide test credentials or sandbox environment
`endpoint_not_found`	Target path returned 404	Check the agent's API routes
`deployment_unusable`	Agent returned 5xx	Check for missing env vars or dependencies
`boot_timeout`	Agent never became reachable	Ensure it binds to PORT and starts an HTTP server
`deployment_failed`	Docker build failed	Provide a working Dockerfile

Trust manifest (optional)

Agents can ship a trust_manifest.yaml declaring what they do. AgentGate compares it against runtime behavior. Without one, everything is inferred from source.

agent_name: My Support Agent
version: "1.0.0"
entrypoint: server.py
declared_tools: [lookup_order, search_products, check_return_policy]
declared_external_domains: []
permissions: [read_orders, read_products]

Self-Hosting

Deploy your own AgentGate instance

AgentGate runs as two services (API + worker) backed by Postgres and Redis.

pip install -e ".[server]"

# Start the API
DATABASE_URL="postgresql://..." REDIS_URL="redis://..." \
  uvicorn agentgate.server.app:create_app --factory --port 8000

# Start the worker (separate terminal)
DATABASE_URL="postgresql://..." REDIS_URL="redis://..." \
  arq agentgate.worker.settings.WorkerSettings

# Create an API key
agentgate api-key create --name "my-key" --database-url "postgresql://..."

Or deploy via Docker on Railway:

Dockerfile.api — API service (port 8000)
Dockerfile.worker — background worker

Required env vars: DATABASE_URL, REDIS_URL

Railway pool mode (reuses a single project instead of creating one per scan):

AGENTGATE_RAILWAY_POOL_PROJECT_ID=<your-project-id>
AGENTGATE_RAILWAY_POOL_ENVIRONMENT=production
AGENTGATE_RAILWAY_POOL_SERVICE=submission-agent

Platform credentials (injected into deployed agents so they can respond to probes):

AGENTGATE_PLATFORM_OPENAI_API_KEY=sk-...       # For agents that use OpenAI
AGENTGATE_PLATFORM_ANTHROPIC_API_KEY=sk-ant-... # For agents that use Anthropic
AGENTGATE_PLATFORM_SLACK_BOT_TOKEN=xoxb-...     # For Slack bot testing
AGENTGATE_PLATFORM_SHOPIFY_ACCESS_TOKEN=shpat_  # For Shopify app testing

Other optional: AGENTGATE_WEBHOOK_SECRET, AGENTGATE_CORS_ORIGINS, AGENTGATE_ADAPTIVE_TRUST, ANTHROPIC_API_KEY (for AgentGate's own specialist LLM calls)

Architecture

agentgate/
  server/            # FastAPI API service (Dockerfile.api)
    app.py           # Lifespan, error envelope, CORS, rate limiting
    routes/          # /v1/scans, /v1/health
    db.py            # Postgres via asyncpg
    webhook.py       # HMAC-signed delivery with SSRF guard
  worker/            # arq background worker (Dockerfile.worker)
    tasks.py         # Scan job orchestration
  services/
    scan_runner.py   # Unified pipeline: source review → deploy → live attack → adaptive review → merge
  trust/
    scanner.py       # Trust check orchestration
    checks/          # 11 trust checks (5 static + 6 runtime)
    runtime/         # Railway executor, adaptive specialists
    policy.py        # Verdict policy engine
    file_classifier.py  # Classify files as test/runtime/docs/vendored
    reachability.py     # Import graph walker from Docker entrypoint
    normalizer.py       # Severity adjustment based on file/reachability context
    signals.py          # TrustSignal and SignalContext models
    destination_taxonomy.py  # Egress destination classification
  detectors/         # 12 security detectors
  scanner.py         # Security scan orchestrator
dashboard.html       # Browser-based scan UI (no build step)

API Features

Input validation with SSRF protection — rejects private IPs, localhost, DNS rebinding
Consistent error envelope — every error returns {"error": "<code>", "detail": "<message>"}
Per-API-key rate limiting on scan creation (10/min)
Deep health check — probes Postgres and Redis, returns 503 if either is down
Typed failure reasons — auth_required, endpoint_not_found, deployment_unusable, boot_timeout, deployment_failed
Human-readable failure explanations — every failure includes a title, description, and actionable next step
Coverage reporting — coverage_status (full/partial/limited) and coverage_recommendation on every response and webhook
Auto env var detection — scans source code for os.getenv() calls and generates sandbox values for missing env vars so agents can start
Webhook delivery with HMAC-SHA256 signing and DNS-resolution SSRF guard
SSE event streaming with resumable cursors via Last-Event-ID
Idempotency keys for exactly-once scan creation
Railway pool mode — reuses a single Railway project for all scans instead of creating ephemeral projects

How It Works — Deep Dive

Stage 1 — Intake

flowchart LR
    A["POST /v1/scans\n+ API key"] --> B["Validate repo_url\nSSRF check\nRate limit"]
    B --> C["Clone repo\nfrom GitHub"]
    C --> D{"Manifest?"}
    D -->|"Yes"| E["Load as hints"]
    D -->|"No"| F["Infer from source"]
    E --> G["Queue scan job"]
    F --> G

    style A fill:#a5d8ff,stroke:#4a9eed
    style G fill:#a5d8ff,stroke:#4a9eed

Stage 2 — Static Analysis

Runs on source code before any deployment. Zero LLM cost.

flowchart LR
    A["Source\nCode"] --> B["Code Signals\nexec, subprocess,\nimportlib, socket,\nbase64, requests"]
    A --> C["Prompt Inspection\nhidden instructions,\noverride phrases,\nexfil directives"]
    A --> D["Dependency Risk\ntyposquats,\nmissing lockfiles,\nmalicious packages"]
    A --> E["Auth Detection\nDepends, login_required,\nAuthorization header,\njwt, HTTPBearer"]
    A --> F["Env Inference\nenv vars, framework,\nports, probe paths,\nintegrations"]

    style A fill:#d0bfff,stroke:#8b5cf6
    style B fill:#d0bfff,stroke:#8b5cf6
    style C fill:#d0bfff,stroke:#8b5cf6
    style D fill:#d0bfff,stroke:#8b5cf6
    style E fill:#d0bfff,stroke:#8b5cf6
    style F fill:#d0bfff,stroke:#8b5cf6

Stage 3 — Deploy to Sandbox

Fills missing config with safe defaults and deploys the agent to an isolated Railway environment.

flowchart TD
    A["Fill missing env vars\nwith sandbox values"] --> B["Inject platform credentials\nSlack, Shopify, OpenAI, Anthropic"]
    B --> C["Build Docker image\non Railway"]
    C --> D["Wait for HTTP\nhealth check"]
    D --> E{"Agent\nresponse?"}
    E -->|"200 OK"| F["Ready for probing"]
    E -->|"401 / 403"| G["auth_required\nAgent needs credentials\nnot available in sandbox"]
    E -->|"5xx errors"| H["deployment_unusable\nAgent crashed or\nmissing dependencies"]
    E -->|"No response"| I["boot_timeout\nAgent never started\nor wrong PORT"]

    style F fill:#b2f2bb,stroke:#22c55e,stroke-width:3px
    style G fill:#ffc9c9,stroke:#ef4444
    style H fill:#ffc9c9,stroke:#ef4444
    style I fill:#ffc9c9,stroke:#ef4444

Stage 4 — Live Probing

Discovers the agent's real API surface, then attacks it.

flowchart TD
    A["Fetch /openapi.json"] --> B["Discover POST endpoints\nResolve request field\nfrom schema"]
    B --> C["Run 12 security detectors\nagainst live endpoint\n(heuristic — no LLM cost)"]

    C --> D["Prompt Injection\nDAN jailbreaks, role-play,\ninstruction overrides"]
    C --> E["Data Exfiltration\nTry to steal secrets\nand credentials"]
    C --> F["Tool Misuse\nInvoke tools outside\ntheir declared scope"]
    C --> G["+ 9 more detectors\nXPIA, goal hijacking,\nsystem prompt leak, ..."]

    D --> H{"Adaptive\nspecialists\nenabled?"}
    E --> H
    F --> H
    G --> H

    H -->|"Yes"| I["Egress Prober — trigger undeclared network calls"]
    H -->|"Yes"| J["Canary Stresser — seed fake creds, check for leaks"]
    H -->|"Yes"| K["Tool Exerciser — enumerate all tool capabilities"]
    H -->|"Yes"| L["Data Boundary — test cross-tenant isolation"]
    H -->|"Yes"| M["Behavior Consistency — compare across runs"]
    H -->|"Yes"| N["Memory Poisoning — mutate state between sessions"]
    H -->|"No"| O["Skip specialists\n$0.00 LLM cost"]

    style A fill:#b2f2bb,stroke:#22c55e
    style I fill:#c3fae8,stroke:#06b6d4
    style J fill:#c3fae8,stroke:#06b6d4
    style K fill:#c3fae8,stroke:#06b6d4
    style L fill:#c3fae8,stroke:#06b6d4
    style M fill:#c3fae8,stroke:#06b6d4
    style N fill:#c3fae8,stroke:#06b6d4

Stage 5 — Verdict

flowchart TD
    A["Merge all findings\nstatic + live + adaptive"] --> B["Apply trust policy\nseverity weights\nevidence strength"]
    B --> C["ALLOW CLEAN\nNo issues found"]
    B --> D["ALLOW WITH WARNINGS\nMinor issues\ne.g. missing lockfile"]
    B --> E["MANUAL REVIEW\nConcerning signals\ne.g. hidden instructions"]
    B --> F["BLOCK\nCritical issues:\nstolen creds, undeclared\negress, hidden behavior"]

    style C fill:#b2f2bb,stroke:#22c55e,stroke-width:3px
    style D fill:#fff3bf,stroke:#f59e0b,stroke-width:3px
    style E fill:#d0bfff,stroke:#8b5cf6,stroke-width:3px
    style F fill:#ffc9c9,stroke:#ef4444,stroke-width:3px

Deployment & Configuration

CLI (alternative to hosted API)

pip install -e .

# Trust scan against a live agent
agentgate trust-scan --url https://my-agent.example.com --source-dir ./src --format all

# Red team security test
agentgate scan http://localhost:8000/api --name "My Agent" --format all

CI/CD integration

SCAN_ID=$(curl -s -X POST https://your-api/v1/scans \
  -H "X-API-Key: $AGENTGATE_KEY" -H "Content-Type: application/json" \
  -d "{\"repo_url\": \"$REPO_URL\"}" | jq -r '.id')

while true; do
  STATUS=$(curl -s -H "X-API-Key: $AGENTGATE_KEY" \
    https://your-api/v1/scans/$SCAN_ID | jq -r '.status')
  [ "$STATUS" = "completed" ] || [ "$STATUS" = "failed" ] && break
  sleep 5
done

VERDICT=$(curl -s -H "X-API-Key: $AGENTGATE_KEY" \
  https://your-api/v1/scans/$SCAN_ID | jq -r '.verdict')
[ "$VERDICT" = "block" ] && exit 1

SARIF output for GitHub Advanced Security: agentgate trust-scan --url $URL --fail-on block --format sarif

Requirements

Python 3.11+ | Postgres + Redis for the hosted API | Railway account for sandbox deployment | cosign (optional) for image signatures | Anthropic API key (optional) for adaptive specialists

Known limitations

Python agents only. Node.js and Go are architecturally supported but not yet implemented.
Static analysis is regex-based. Catches exec() and requests.post() but not obfuscated equivalents — that's what runtime checks are for.
Deploy timeout. Large repos with heavy Docker builds may time out during sandbox deployment.

Development

pip install -e ".[dev,server]"
uv run pytest tests/ -x -q       # 494 tests
uv run ruff check src/

Docs

docs/promptshop_engineering_brief.md — engineering architecture brief
docs/trust_benchmarking.md — benchmark harness and results
docs/ci_integration.md — CI/CD integration guide
docs/owasp_coverage.md — OWASP LLM Top 10 coverage mapping
docs/superpowers/specs/2026-03-26-hosted-api-design.md — hosted API architecture
docs/superpowers/specs/2026-03-29-finding-interpretation-design.md — finding interpretation and confidence-aware verdicts

Name		Name	Last commit message	Last commit date
Latest commit History 85 Commits
.github/workflows		.github/workflows
assets		assets
benchmarks		benchmarks
demo_agents		demo_agents
docs		docs
examples		examples
research		research
scripts		scripts
src/agentgate		src/agentgate
test_agents		test_agents
tests		tests
.gitignore		.gitignore
Dockerfile.api		Dockerfile.api
Dockerfile.worker		Dockerfile.worker
LICENSE		LICENSE
README.md		README.md
action.yml		action.yml
dashboard.html		dashboard.html
pyproject.toml		pyproject.toml
railway_discovered_manifest.yaml		railway_discovered_manifest.yaml
uv.lock		uv.lock

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