curl --request POST \
  --url https://api.enkryptai.com/guardrails/scan-url \
  --header 'Content-Type: application/json' \
  --header 'apikey: <api-key>' \
  --data '
{
  "url": "<string>",
  "detectors": {},
  "user_metadata": {
    "request_id": "abc-123",
    "source": "mobile-app"
  }
}
'

import requests

url = "https://api.enkryptai.com/guardrails/scan-url"

payload = {
    "url": "<string>",
    "detectors": {},
    "user_metadata": {
        "request_id": "abc-123",
        "source": "mobile-app"
    }
}
headers = {
    "apikey": "<api-key>",
    "Content-Type": "application/json"
}

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

print(response.text)

const options = {
  method: 'POST',
  headers: {apikey: '<api-key>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    url: '<string>',
    detectors: {},
    user_metadata: {request_id: 'abc-123', source: 'mobile-app'}
  })
};

fetch('https://api.enkryptai.com/guardrails/scan-url', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://api.enkryptai.com/guardrails/scan-url",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_POSTFIELDS => json_encode([
    'url' => '<string>',
    'detectors' => [
        
    ],
    'user_metadata' => [
        'request_id' => 'abc-123',
        'source' => 'mobile-app'
    ]
  ]),
  CURLOPT_HTTPHEADER => [
    "Content-Type: application/json",
    "apikey: <api-key>"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://api.enkryptai.com/guardrails/scan-url"

	payload := strings.NewReader("{\n  \"url\": \"<string>\",\n  \"detectors\": {},\n  \"user_metadata\": {\n    \"request_id\": \"abc-123\",\n    \"source\": \"mobile-app\"\n  }\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("apikey", "<api-key>")
	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.post("https://api.enkryptai.com/guardrails/scan-url")
  .header("apikey", "<api-key>")
  .header("Content-Type", "application/json")
  .body("{\n  \"url\": \"<string>\",\n  \"detectors\": {},\n  \"user_metadata\": {\n    \"request_id\": \"abc-123\",\n    \"source\": \"mobile-app\"\n  }\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.enkryptai.com/guardrails/scan-url")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["apikey"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"url\": \"<string>\",\n  \"detectors\": {},\n  \"user_metadata\": {\n    \"request_id\": \"abc-123\",\n    \"source\": \"mobile-app\"\n  }\n}"

response = http.request(request)
puts response.read_body

{
  "summary": {
    "injection_attack": 1,
    "bias": 1,
    "policy_violation": 0
  },
  "details": {
    "url": "https://example.com",
    "metadata": {
      "title": "Example Domain",
      "language": "en"
    },
    "total_fragments": 2,
    "fragments_with_detections": 2,
    "detections": [
      {
        "unsafe_content": "Example Domain",
        "chunk_type": "title",
        "triggered_detectors": [
          "bias"
        ],
        "detector_details": {
          "injection_attack": {
            "safe": "0.999417",
            "attack": "0.000583",
            "most_unsafe_content": "Example Domain",
            "compliance_mapping": {}
          },
          "policy_violation": {
            "policy_violation": {
              "violating_policy": "No Violation Found",
              "explanation": "No Violation Found"
            }
          },
          "bias": {
            "bias": {
              "bias_detected": true,
              "debiased_text": "Example Website",
              "compliance_mapping": {
                "owasp_llm_2025": [
                  "LLM09:2025 Misinformation",
                  "LLM04:2025 Data and Model Poisoning"
                ],
                "mitre_atlas": [],
                "nist_ai_rmf": [
                  "MEASURE 2.1-2.5 (AI system bias evaluation & management)"
                ],
                "eu_ai_act": [
                  "Article 10(2)(f), Article 15(3) (Bias detection, correction & mitigation)"
                ],
                "iso_iec_standards": [
                  "ISO/IEC TR 24027: 5.2 (Bias in AI systems)"
                ]
              }
            }
          }
        }
      },
      {
        "unsafe_content": "Example Domain This domain is for use in documentation examples without needing permission. Avoid use in operations. Learn more",
        "chunk_type": "text_block",
        "triggered_detectors": [
          "injection_attack",
          "bias"
        ],
        "detector_details": {
          "injection_attack": {
            "safe": "0.030481",
            "attack": "0.969519",
            "most_unsafe_content": "Example Domain This domain is for use in documentation examples without needing permission. Avoid use in operations. Learn more",
            "compliance_mapping": {
              "owasp_llm_2025": [
                "LLM01:2025 Prompt Injection"
              ],
              "mitre_atlas": [
                "AML.T0051: LLM Prompt Injection",
                "AML.T0054: LLM Jailbreaking"
              ],
              "nist_ai_rmf": [
                "MAP 2.3, MEASURE 2.3 (Input manipulation & adversarial attacks)"
              ],
              "eu_ai_act": [
                "Article 15(4) (Robustness against manipulation)"
              ],
              "iso_iec_standards": [
                "ISO/IEC 42001: 6.4.3",
                "ISO/IEC 27001: A.14.2"
              ]
            }
          },
          "policy_violation": {
            "policy_violation": {
              "violating_policy": "No Violation Found",
              "explanation": "No Violation Found"
            }
          },
          "bias": {
            "bias": {
              "bias_detected": true,
              "debiased_text": "This domain is intended for use in documentation purposes, providing a safe space for experimentation without impacting live systems.",
              "compliance_mapping": {
                "owasp_llm_2025": [
                  "LLM09:2025 Misinformation",
                  "LLM04:2025 Data and Model Poisoning"
                ],
                "mitre_atlas": [],
                "nist_ai_rmf": [
                  "MEASURE 2.1-2.5 (AI system bias evaluation & management)"
                ],
                "eu_ai_act": [
                  "Article 10(2)(f), Article 15(3) (Bias detection, correction & mitigation)"
                ],
                "iso_iec_standards": [
                  "ISO/IEC TR 24027: 5.2 (Bias in AI systems)"
                ]
              }
            }
          }
        }
      }
    ],
    "combined_highlight_url": "https://example.com#:~:text=Example%20Domain&text=Example%20Domain%20This,operations.%20Learn%20more"
  }
}