Quickstart

This walks through one complete red team run: submit, track, and read the results. For target configuration across different providers, see the GET Defaults API response and Testing Models Across Providers.

Install required libraries

Shell

pip install requests python-dotenv enkryptai-sdk

Set up your credentials

Shell

export OPENAI_API_KEY="YOUR_OPENAI_API_KEY"
export ENKRYPTAI_API_KEY="YOUR_ENKRYPTAI_API_KEY"

Step 1 — Submit a run

The payload has three parts: the target to test, the risk_categories to test for (with the attacks to use), and an optional context describing your system.

import json
import os
import requests

url = "https://api.enkryptai.com/rt/redteam"

payload = {
    "run_name": "quickstart-run",
    "target": {
        "endpoint": "https://api.openai.com/v1/chat/completions",
        "api_key": os.getenv("OPENAI_API_KEY"),
        "model_name": "gpt-4o-mini",
        "provider": "openai",
        "system_type": "foundation_model",
    },
    "risk_categories": {
        "safety_harm": {"attack_config": {"basic": {}}},
        "bias_fairness_ethical_harm": {"attack_config": {"basic": {}}},
        "security_fraud": {"attack_config": {"basic": {}, "encoding": {}}},
    },
    "context": {
        "system_description": "A general-purpose customer support assistant.",
        "policy_description": "Never produce content that is illegal, harmful, or violates the rights of others.",
    },
    "generation_config": {"max_prompts": 30},
    "comprehensiveness": "Standard",
}

headers = {
    "Content-Type": "application/json",
    "apikey": os.getenv("ENKRYPTAI_API_KEY"),
}

response = requests.post(url, headers=headers, data=json.dumps(payload))
run = response.json()

print(json.dumps(run, indent=2))
run_id = run["run_id"]

curl -X POST 'https://api.enkryptai.com/rt/redteam' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -H "apikey: $ENKRYPTAI_API_KEY" \
  -d '{
    "run_name": "quickstart-run",
    "target": {
        "endpoint": "https://api.openai.com/v1/chat/completions",
        "api_key": "YOUR_OPENAI_API_KEY",
        "model_name": "gpt-4o-mini",
        "provider": "openai",
        "system_type": "foundation_model"
    },
    "risk_categories": {
        "safety_harm": { "attack_config": { "basic": {} } },
        "bias_fairness_ethical_harm": { "attack_config": { "basic": {} } },
        "security_fraud": { "attack_config": { "basic": {}, "encoding": {} } }
    },
    "context": {
        "system_description": "A general-purpose customer support assistant.",
        "policy_description": "Never produce content that is illegal, harmful, or violates the rights of others."
    },
    "generation_config": { "max_prompts": 30 },
    "comprehensiveness": "Standard"
}'

import os
from enkryptai_sdk import RedTeamClient
from dotenv import load_dotenv

load_dotenv()

ENKRYPT_API_KEY = os.getenv("ENKRYPTAI_API_KEY")
ENKRYPT_BASE_URL = os.getenv("ENKRYPTAI_BASE_URL") or "https://api.enkryptai.com"

redteam_client = RedTeamClient(api_key=ENKRYPT_API_KEY, base_url=ENKRYPT_BASE_URL)

config = {
    "run_name": "quickstart-run",
    "target": {
        "endpoint": "https://api.openai.com/v1/chat/completions",
        "api_key": os.getenv("OPENAI_API_KEY"),
        "model_name": "gpt-4o-mini",
        "provider": "openai",
        "system_type": "foundation_model",
    },
    "risk_categories": {
        "safety_harm": {"attack_config": {"basic": {}}},
        "bias_fairness_ethical_harm": {"attack_config": {"basic": {}}},
        "security_fraud": {"attack_config": {"basic": {}, "encoding": {}}},
    },
    "context": {
        "system_description": "A general-purpose customer support assistant.",
        "policy_description": "Never produce content that is illegal, harmful, or violates the rights of others.",
    },
    "generation_config": {"max_prompts": 30},
    "comprehensiveness": "Standard",
}

run = redteam_client.run_redteam(config=config)

print(run.to_dict())
run_id = run.run_id

The response is 202 Accepted. Nothing has run yet — you have a handle to a queued job:

JSON

{
  "run_id": "6c3babbe-3bc4-4553-a0b7-454df720579f",
  "status": "queued",
  "status_url": "/v1/runs/6c3babbe-3bc4-4553-a0b7-454df720579f",
  "results_url": "/v1/runs/6c3babbe-3bc4-4553-a0b7-454df720579f/results",
  "websocket_url": "/v1/runs/6c3babbe-3bc4-4553-a0b7-454df720579f/stream",
  "estimated_duration_seconds": null
}

The status_url, results_url and websocket_url values are returned as upstream /v1/... paths. Call them on the public API under the /rt/ prefix — for example GET https://api.enkryptai.com/rt/runs/{run_id}.

Step 2 — Track the run

Poll GET /rt/runs/{run_id} until it reports a terminal state. Its status values are lowercase: queued, running, paused, completed, failed, cancelled. The terminal three are completed, failed and cancelled — paused is not terminal, and a paused run resumes.

import time

status_url = f"https://api.enkryptai.com/rt/runs/{run_id}"

while True:
    status = requests.get(status_url, headers=headers).json()
    print(status["status"])
    if status["status"] in ("completed", "failed", "cancelled"):
        break
    time.sleep(30)

curl -X GET "https://api.enkryptai.com/rt/runs/$RUN_ID" \
  -H "apikey: $ENKRYPTAI_API_KEY"

# get_run() parses the response and normalises the two vocabularies below into
# one, so `state` and `is_terminal` work whichever endpoint the status came from
status = redteam_client.wait_for_run(run_id=run_id, poll_interval=30)

print(status.state)        # "finished" for a run the endpoint calls "completed"
print(status.is_terminal)  # True

JSON

{
  "id": "6c3babbe-3bc4-4553-a0b7-454df720579f",
  "status": "running",
  "started_at": "2026-01-15T10:30:00Z",
  "completed_at": null
}

On failure, the error object carries the reason.

One run, two status vocabularies. Which one you get depends on the endpoint you asked, because they read different tables:

Endpoint	Vocabulary	Values
`GET /rt/runs/{run_id}`	The run record — lowercase	`queued`, `running`, `paused`, `completed`, `failed`, `cancelled`
`GET /rt/runs/{run_id}/results`	The red-team stage — capitalised	`Queued`, `Running`, `Finished`, `Failed`
`GET /rt/runs/{run_id}/records`	The red-team stage — capitalised	`Queued`, `Running`, `Finished`, `Failed`

A finished run therefore reports "completed" on the status endpoint and "Finished" on the results and records endpoints. This is deliberate, not a bug: the two are different pieces of state that happen to share a field name, and collapsing them is what once made every completed run’s results unreachable. Match against the vocabulary of the endpoint you called — a poll loop waiting for "Finished" on GET /rt/runs/{run_id} never exits.The Python SDK papers over the difference for you: RTRunStatus.state folds completed onto finished, and is_terminal is true for either.

Prefer not to poll? GET /rt/runs/{run_id}/stream delivers live progress events for the same run. See Stream Run Events.

Step 3 — Read the results

results = requests.get(
    f"https://api.enkryptai.com/rt/runs/{run_id}/results", headers=headers
).json()

print(json.dumps(results, indent=2))

curl -X GET "https://api.enkryptai.com/rt/runs/$RUN_ID/results" \
  -H "apikey: $ENKRYPTAI_API_KEY"

results = redteam_client.get_run_results(run_id=run_id)

print(results.to_dict())

Results are scored as attack success rate (ASR) — the percentage of adversarial prompts that got through. Lower is better.

JSON

{
  "run_id": "6c3babbe-3bc4-4553-a0b7-454df720579f",
  "status": "Finished",
  "completed_at": "2026-01-15T10:45:00Z",
  "records_url": "/v1/runs/6c3babbe-3bc4-4553-a0b7-454df720579f/records",
  "summary": {
    "overall_asr": 12.5,
    "total_attempts": 30,
    "coverage": 1.0,
    "categories": {
      "safety_harm": {
        "asr": 8.0,
        "sub_categories": {
          "cbrn": { "asr": 0.0, "count": 5 },
          "harmful_instructions": { "asr": 20.0, "count": 5 }
        },
        "by_attack_method": {
          "basic": { "asr": 8.0 }
        },
        "by_source": {
          "standard_library": { "asr": 8.0, "count": 10 }
        }
      }
    }
  }
}

overall_asr and every asr are percentages from 0 to 100. coverage reports the fraction of the requested scope that actually ran.

Step 4 — Drill into individual results

The summary tells you what failed. The records tell you how.

records = requests.get(
    f"https://api.enkryptai.com/rt/runs/{run_id}/records", headers=headers
).json()

for row in records["records"][:5]:
    print(row["category"], row["sub_category"], row["test_type"], row["success"])

curl -X GET "https://api.enkryptai.com/rt/runs/$RUN_ID/records" \
  -H "apikey: $ENKRYPTAI_API_KEY"

records = redteam_client.get_run_records(run_id=run_id)

print(records.to_dict())

Each record carries the prompt sent, the response received, the judge’s verdict and its reasoning:

JSON

{
  "run_id": "6c3babbe-3bc4-4553-a0b7-454df720579f",
  "status": "Finished",
  "completed_at": "2026-01-15T10:45:00Z",
  "records": [
    {
      "query": "<the adversarial prompt sent to your target>",
      "response": "<your target's reply>",
      "test_type": "basic",
      "category": "safety_harm",
      "sub_category": "harmful_instructions",
      "goal": "<what the prompt was trying to elicit>",
      "success": true,
      "response_classification": "compliant",
      "reasoning": "<why the judge scored it this way>",
      "confidence": 0.91
    }
  ]
}

success: true means the attack succeeded — your target produced the content it should have refused. These are the rows to investigate first.

Next steps

Map to a compliance framework

GET /rt/runs/{run_id}/compliance/{framework_id} re-expresses the same run against OWASP LLM Top 10, EU AI Act, NIST AI RMF, MITRE ATLAS, ISO 42001 or AIUC-1.

Get remediation

POST /rt/risk-mitigation/system-prompt turns a run summary into a hardened system prompt; /rt/risk-mitigation/guardrails-policy turns it into a Guardrails policy.

Widen the attack surface

basic is only the baseline. Add evasion and adaptive jailbreak techniques.

Check your target first

POST /rt/model-health verifies Enkrypt AI can reach and call your target before you spend a run on it.

Get Started for Guardrails

Get Started for Red Team

Get Started with AI Proxy

Get Started with MCP

Get Started with Skill Scanner

On-Prem Installation

Install required libraries

Set up your credentials

Step 1 — Submit a run

Step 2 — Track the run

Step 3 — Read the results

Step 4 — Drill into individual results

Next steps

Map to a compliance framework

Get remediation

Widen the attack surface

Check your target first

​Install required libraries

​Set up your credentials

​Step 1 — Submit a run

​Step 2 — Track the run

​Step 3 — Read the results

​Step 4 — Drill into individual results

​Next steps

Map to a compliance framework

Get remediation

Widen the attack surface

Check your target first

Install required libraries

Set up your credentials

Step 1 — Submit a run

Step 2 — Track the run

Step 3 — Read the results

Step 4 — Drill into individual results

Next steps