Payload Guide for EnkryptAI Red Teaming API (V3)

The payload for EnkryptAI’s Red Teaming API V3 is structured as a JSON object with three primary sections: dataset_configuration, redteam_test_configurations, and endpoint_configuration.

Attack Methods

Comprehensive guide to all attack methods

Configuration

Detailed configuration reference

Examples

Ready-to-use payload examples

What’s New in V3

V3 introduces significant enhancements to attack methods configuration:

Granular Parameter Control: Each attack method now supports specific parameters for fine-tuned testing
Structured Attack Hierarchy: Clear organization of basic, static, and dynamic attack methods
Enhanced Attack Methods: Expanded suite of encoding, obfuscation, and multi-modal attack techniques

Migrating from V2? Click here for migration guide

V2 to V3 Migration

V2 Format (Legacy):

{
  "redteam_test_configurations": {
    "privacy_test": {
      "sample_percentage": 50,
      "attack_methods": {
        "basic": ["basic"],
        "advanced": {
          "static": ["encoding"],
          "dynamic": ["iterative"]
        }
      }
    }
  }
}

V3 Format (Current):

{
  "redteam_test_configurations": {
    "privacy_test": {
      "sample_percentage": 50,
      "attack_methods": {
        "basic": {"basic": {"params": {}}},
        "static": {
          "base64_encoding": {
            "params": {"encoding_type": "base64", "iterations": 2}
          }
        },
        "dynamic": {
          "iterative": {
            "params": {
              "width": 5,
              "branching_factor": 9,
              "depth": 3
            }
          }
        }
      }
    }
  }
}

Key Changes:

Attack methods are now objects with params instead of arrays
Each attack method requires a params object (can be empty {})
Use specific encoding keywords (e.g., base64_encoding instead of generic encoding)
Configure parameters for iterative attacks: width, branching_factor, depth

Quick Reference: Attack Methods

Choose attack methods based on your model type and security testing needs. This table provides an enhanced overview of all available attack methods.

By Model Type

LLM (Text)
VLM (Vision)
ALM (Audio)

Attack Method	Keyword	Category	Use When	Parameters	Complexity
Raw Prompts	`basic`	Basic	Always (baseline)	None	⭐ Low
Obfuscation	`obfuscation`	Static	Standard testing	None	⭐⭐ Medium
Base64	`base64_encoding`	Static	Encoding evasion	`iterations` (1-3)	⭐⭐ Medium
Hexadecimal	`hex_encoding`	Static	Technical filters	None	⭐⭐ Medium
ASCII	`ascii_encoding`	Static	Text filters	None	⭐⭐ Medium
Binary	`binary_encoding`	Static	Advanced encoding	None	⭐⭐ Medium
URL Encoding	`url_encoding`	Static	Web applications	None	⭐ Low
Leet Speak	`leet_encoding`	Static	Character matching	None	⭐ Low
ROT13	`rot13_encoding`	Static	Cipher testing	None	⭐ Low
ROT21	`rot21_encoding`	Static	Cipher testing	None	⭐ Low
Morse Code	`morse_encoding`	Static	Unique encoding	None	⭐⭐ Medium
French	`lang_fr`	Static	Multilingual bypass	None	⭐⭐ Medium
Italian	`lang_it`	Static	Multilingual bypass	None	⭐⭐ Medium
Hindi	`lang_hi`	Static	Non-Latin scripts	None	⭐⭐ Medium
Spanish	`lang_es`	Static	Multilingual bypass	None	⭐⭐ Medium
Japanese	`lang_ja`	Static	Asian languages	None	⭐⭐ Medium
EAI Attack	`eai_attack`	Static	Advanced jailbreak	None	⭐⭐⭐ High
Deep Inception	`deep_inception`	Static	Nested injection	None	⭐⭐⭐ High
Iterative	`iterative`	Dynamic	Adaptive attacks	`width`, `branching_factor`, `depth`	⭐⭐⭐ High
Multi-Turn	`multi_turn`	Dynamic	Conversation exploit	None	⭐⭐⭐ High

Quick Lookup: All Keywords

Basic & Obfuscation (3 methods)

basic - LLM, VLM, ALM
obfuscation - LLM, VLM
eai_attack - LLM, VLM

Encoding (9 methods)

ascii_encoding - LLM
base64_encoding - LLM
binary_encoding - LLM
hex_encoding - LLM
url_encoding - LLM
leet_encoding - LLM
morse_encoding - LLM
rot13_encoding - LLM
rot21_encoding - LLM

Multilingual (5 methods)

lang_fr - French (LLM)
lang_it - Italian (LLM)
lang_hi - Hindi (LLM)
lang_es - Spanish (LLM)
lang_ja - Japanese (LLM)

Dynamic (2 methods)

iterative - LLM
multi_turn - LLM

Visual (4 methods)

masking - VLM
figstep - VLM
hades 🔒 - VLM
jood 🔒 - VLM

Audio (6 methods)

waveform - ALM
echo - ALM
speed - ALM
pitch - ALM
reverb - ALM
noise - ALM

Advanced (1 method)

deep_inception - LLM

Payload Structure

High-Level Overview

{
    "dataset_configuration": {
        // Optional: For generating custom datasets
        "system_description": "Your AI system description",
        "policy_description": "What the model should NOT do",
        "max_prompts": 100,
        "scenarios": 2,
        "categories": 2,
        "depth": 2
    },
    "redteam_test_configurations": {
        // Required: Tests to run
        "test_name": {
            "sample_percentage": 10,
            "attack_methods": {
                "basic": {"basic": {"params": {}}},
                "static": { /* encoding/obfuscation methods */ },
                "dynamic": { /* adaptive attack methods */ }
            }
        }
    },
    "endpoint_configuration": {
        // Required: Model to test
        "testing_for": "foundationModels",
        "model_name": "gpt-4o",
        "model_config": {
            "model_provider": "openai",
            "endpoint": { /* API endpoint details */ },
            "auth_data": { /* Authentication config */ },
            "apikeys": ["YOUR_API_KEY"],
            "input_modalities": ["text"],
            "output_modalities": ["text"]
        }
    }
}

For complete field descriptions, see Configuration Reference.

Quick Start Examples

Starter: Basic LLM Test

{
  "redteam_test_configurations": {
    "harmful_test": {
      "sample_percentage": 10,
      "attack_methods": {
        "basic": {"basic": {"params": {}}}
      }
    }
  },
  "endpoint_configuration": {
    "testing_for": "foundationModels",
    "model_name": "gpt-4o-mini",
    "model_config": {
      "model_provider": "openai",
      "endpoint_url": "https://api.openai.com/v1/",
      "apikey": "YOUR_OPENAI_API_KEY",
      "input_modalities": ["text"],
      "output_modalities": ["text"]
    }
  }
}

Standard: Multi-Test Assessment

{
  "redteam_test_configurations": {
    "harmful_test": {
      "sample_percentage": 20,
      "attack_methods": {
        "basic": {"basic": {"params": {}}},
        "static": {
          "obfuscation": {"params": {}},
          "base64_encoding": {"params": {"encoding_type": "base64", "iterations": 1}}
        }
      }
    },
    "bias_test": {
      "sample_percentage": 15,
      "attack_methods": {
        "basic": {"basic": {"params": {}}},
        "static": {"lang_es": {"params": {}}}
      }
    },
    "pii_test": {
      "sample_percentage": 20,
      "attack_methods": {
        "basic": {"basic": {"params": {}}}
      }
    }
  },
  "endpoint_configuration": {
    "testing_for": "foundationModels",
    "model_name": "gpt-4o",
    "model_config": {
      "model_provider": "openai",
      "endpoint_url": "https://api.openai.com/v1/",
      "apikey": "YOUR_OPENAI_API_KEY",
      "input_modalities": ["text"],
      "output_modalities": ["text"]
    }
  }
}

For more examples including VLM, ALM, agents, and industry-specific use cases, see the Examples page.

Available Test Types

Standard Tests (12 tests)

Tests available for all model types:

Test	Keyword	Purpose
Bias	`bias_test`	Identifies biased outputs
CBRN	`cbrn_test`	Chemical, biological, radiological, nuclear
CSEM	`csem_test`	Child abuse exploitation
Harmful	`harmful_test`	Harm or danger promotion
Insecure Code	`insecure_code_test`	Vulnerable code generation
Toxicity	`toxicity_test`	Offensive content
PII	`pii_test`	Personal information leakage
Copyright	`copyright_test`	Copyrighted material
Misinformation	`misinformation_test`	False information
System Prompt	`system_prompt_extractions_test`	Prompt extraction
Sponge	`sponge_test`	Resource exhaustion
Competitor	`competitor_test`	Competitor information

Custom & Agentic Tests

Custom Test

custom_test - Test with your own generated dataset

Agentic Tests (8 tests)

For AI agents with tool use and autonomous capabilities:

governance_test - Alignment, goal misalignment, and policy drift
agent_output_quality_test - Output quality, hallucinations, bias, and toxicity
tool_misuse_test - API integration, supply chain, and resource consumption
privacy_test - Sensitive data exposure and exfiltration channels
reliability_and_observability_test - Data poisoning, concept drift, and opaque reasoning
agent_behaviour_test - Human manipulation and unsafe actuation
access_control_and_permissions_test - Credential theft, privilege escalation, confused deputy
tool_extraction_test - Tool information extraction

Specialized Tests (6 tests)

For generated adversarial datasets:

adv_bias_test - Adversarial bias detection
adv_info_test - Sensitive information extraction
adv_persona_test - Persona manipulation
adv_command_test - Command injection
adv_pii_test - Advanced PII extraction
adv_competitor_test - Competitor information

Best Practices

Start Simple

Begin with basic attacks at low sample percentage (2-5%) to establish baseline.

Progressive Testing

Add static methods, then dynamic attacks as you identify vulnerabilities.

Match Your Model

Use appropriate modalities: ["text"] for LLM, ["text", "image"] for VLM, ["text", "audio"] for ALM.

Multiple Tests

Run multiple test types (harmful, bias, PII) for comprehensive coverage.

Sample Wisely

Dev: 2-5% | Staging: 10-20% | Production: 50-100%

Consider Cost

Dynamic attacks are thorough but resource-intensive. Start with static methods.

Configuration Guidelines

Sample Percentage by Stage

// Development - Fast iteration
{
  "sample_percentage": 2  // 2-5%
}

// Staging - Balanced testing
{
  "sample_percentage": 15  // 10-20%
}

// Production - Comprehensive
{
  "sample_percentage": 50  // 50-100%
}

Attack Method Combinations

Minimal (Dev)
Standard (Staging)
Comprehensive (Prod)

{
  "attack_methods": {
    "basic": {"basic": {"params": {}}}
  }
}

Time: 2-5 min | Coverage: Baseline

Common Parameters

iterative.width

integer

default:"5"

Number of parallel attack paths (1-10)

iterative.branching_factor

integer

default:"9"

Variations per iteration (1-15)

iterative.depth

integer

default:"3"

Maximum iteration depth (1-5)

base64_encoding.iterations

integer

default:"1"

Encoding iterations (1-3). Higher = more obfuscation but less comprehension

Security & Usage Notes

Security:

Never commit API keys to version control
Use environment variables for credentials
Rotate keys regularly
Separate test and production keys

Usage:

Verify endpoints and keys are correct
Adjust sample_percentage based on dataset size
Choose attack methods appropriate for your model type
Use in accordance with provider terms of service

Next Steps

Attack Methods

Explore detailed attack method documentation with parameters and use cases

Configuration

Complete reference for all configuration fields and options

Examples

Browse ready-to-use examples for different scenarios and providers

API Reference

View the complete API specification and endpoints

Additional Resources

Quickstart Guide - Get started with your first test
GET Defaults API - Supported providers and models
Red Team Introduction - Overview of red teaming concepts

This payload structure facilitates in-depth testing across various model types, allowing for comprehensive assessments of behavior and security with fine-grained control over attack parameters.

Get Started for Guardrails

Get Started for Red Team

Get Started with AI Proxy

Payload Guide for EnkryptAI Red Teaming API (V3)

Attack Methods

Configuration

Examples

What’s New in V3

V2 to V3 Migration

Quick Reference: Attack Methods

By Model Type

Quick Lookup: All Keywords

Payload Structure

High-Level Overview

Quick Start Examples

Starter: Basic LLM Test

Standard: Multi-Test Assessment

Available Test Types

Standard Tests (12 tests)

Custom & Agentic Tests

Best Practices

Start Simple

Progressive Testing

Match Your Model

Multiple Tests

Sample Wisely

Consider Cost

Configuration Guidelines

Sample Percentage by Stage

Attack Method Combinations

Common Parameters

Security & Usage Notes

Next Steps

Attack Methods

Configuration

Examples

API Reference

Additional Resources

Get Started for Guardrails

Get Started for Red Team

Get Started with AI Proxy

Attack Methods

Configuration

Examples

​What’s New in V3

​V2 to V3 Migration

​Quick Reference: Attack Methods

​By Model Type

​Quick Lookup: All Keywords

​Payload Structure

​High-Level Overview

​Quick Start Examples

​Starter: Basic LLM Test

​Standard: Multi-Test Assessment

​Available Test Types

​Standard Tests (12 tests)

​Custom & Agentic Tests

​Best Practices

Start Simple

Progressive Testing

Match Your Model

Multiple Tests

Sample Wisely

Consider Cost

​Configuration Guidelines

​Sample Percentage by Stage

​Attack Method Combinations

​Common Parameters

​Security & Usage Notes

​Next Steps

Attack Methods

Configuration

Examples

API Reference

​Additional Resources

What’s New in V3

V2 to V3 Migration

Quick Reference: Attack Methods

By Model Type

Quick Lookup: All Keywords

Payload Structure

High-Level Overview

Quick Start Examples

Starter: Basic LLM Test

Standard: Multi-Test Assessment

Available Test Types

Standard Tests (12 tests)

Custom & Agentic Tests

Best Practices

Configuration Guidelines

Sample Percentage by Stage

Attack Method Combinations

Common Parameters

Security & Usage Notes

Next Steps

Additional Resources