Example request:
import requests
import json
import os
url = "https://api.enkryptai.com/guardrails/scan-url"
payload = json.dumps({
"url": "https://example.com",
"detectors": {
"injection_attack": {
"enabled": true,
"block_message": "Your custom message"
},
"bias": {
"enabled": true
},
"policy_violation": {
"enabled": true,
"policy_text": "Detect any malicious text or injection attacks",
"block_message": "Your custom message"
}
}
})
headers = {
'Content-Type': 'application/json',
'apikey': os.getenv('ENKRYPTAI_API_KEY')
}
response = requests.post(url, headers=headers, data=payload)
print(response.json())
from enkryptai_sdk import *
client = GuardrailsClient(api_key="YOUR_API_KEY")
config = {
"injection_attack": {
"enabled": True,
"block_message": "Your custom message"
},
"bias": {
"enabled": True
},
"policy_violation": {
"enabled": True,
"policy_text": "Detect any malicious text or injection attacks",
"block_message": "Your custom message"
}
}
result = client.scan_url("https://example.com", config=config)
print(result)
Example response:
JSON
{
"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"
},
"result_message": "Your custom message"
}