Injecting Genesys Cloud Agent Assist LLM Gateway Prompts via WebSocket with Python

What You Will Build

A production-grade Python service that constructs, validates, and streams prompt injection payloads to a Genesys Cloud Agent Assist LLM gateway over WebSocket. The code enforces context window limits, applies jailbreak pattern filtering, serializes payloads into atomic binary frames, synchronizes with external moderation webhooks, and generates governance audit logs. This tutorial uses the genesyscloud-python SDK for REST validation and websockets for binary frame delivery. Python 3.10+ is covered.

Prerequisites

• OAuth 2.0 client credentials flow with scopes: agentassist:gateway:read, agentassist:prompt:write, conversation:read, analytics:events:read
• Genesys Cloud Python SDK v2.0+ (pip install genesyscloud-python)
• Python 3.10+ runtime
• Dependencies: httpx, websockets, pydantic, aiohttp, pyyaml
• A configured LLM gateway ID in your Genesys Cloud organization
• Access to a WebSocket streaming endpoint compatible with Genesys Cloud authentication headers

Authentication Setup

Genesys Cloud OAuth 2.0 client credentials flow requires a region-specific token endpoint. The following code implements token acquisition with automatic refresh logic and retry handling for 429 rate limits.

import httpx
import asyncio
import time
from typing import Optional
import logging

logger = logging.getLogger("genesys_auth")

class GenesysTokenManager:
    def __init__(self, client_id: str, client_secret: str, region: str = "usw2"):
        self.client_id = client_id
        self.client_secret = client_secret
        self.region = region
        self.token_url = f"https://api.{region}.genesyscloud.com/oauth/token"
        self._access_token: Optional[str] = None
        self._expires_at: float = 0.0

    async def get_token(self) -> str:
        if self._access_token and time.time() < self._expires_at - 60:
            return self._access_token

        async with httpx.AsyncClient(timeout=10.0) as client:
            for attempt in range(3):
                try:
                    response = await client.post(
                        self.token_url,
                        headers={"Content-Type": "application/x-www-form-urlencoded"},
                        data={
                            "grant_type": "client_credentials",
                            "client_id": self.client_id,
                            "client_secret": self.client_secret
                        }
                    )
                    response.raise_for_status()
                    data = response.json()
                    self._access_token = data["access_token"]
                    self._expires_at = time.time() + data["expires_in"]
                    return self._access_token
                except httpx.HTTPStatusError as e:
                    if e.response.status_code == 429 and attempt < 2:
                        retry_after = int(e.response.headers.get("Retry-After", 2))
                        logger.warning("Rate limited on token fetch. Retrying in %d seconds.", retry_after)
                        await asyncio.sleep(retry_after)
                    else:
                        raise
        raise RuntimeError("Failed to acquire OAuth token after retries.")

Implementation

Step 1: Fetch Gateway Constraints via REST

Before constructing injection payloads, the service must validate maximum context window limits and supported temperature ranges against the target gateway. This uses the official /api/v2/conversations/assistants/gateways/{gatewayId} endpoint.

from genesyscloud.platform.client import PlatformClientBuilder
from genesyscloud.api.conversations import ConversationsApi

async def fetch_gateway_constraints(region: str, gateway_id: str, token_manager: GenesysTokenManager) -> dict:
    token = await token_manager.get_token()
    builder = PlatformClientBuilder()
    builder.set_base_url(f"https://api.{region}.genesyscloud.com")
    builder.set_auth_token(token)
    platform = builder.build()
    
    conversations_api = ConversationsApi(platform)
    
    try:
        response = await conversations_api.get_conversations_assistants_gateways_gateway_id(gateway_id)
        if response.status_code != 200:
            raise RuntimeError(f"Gateway fetch failed: {response.status_code} - {response.body}")
        
        gateway_data = response.body
        return {
            "max_context_tokens": gateway_data.get("max_context_tokens", 4096),
            "min_temperature": gateway_data.get("temperature_range", {}).get("min", 0.1),
            "max_temperature": gateway_data.get("temperature_range", {}).get("max", 0.9),
            "supported_models": gateway_data.get("supported_models", [])
        }
    except Exception as e:
        logger.error("Failed to fetch gateway constraints: %s", str(e))
        raise

Step 2: Construct Prompt Matrix & Temperature Directives

The inject payload requires an assist ID reference, a system prompt matrix, and temperature scaling directives. Pydantic validates the structure before serialization.

from pydantic import BaseModel, Field, validator
import uuid

class PromptMatrix(BaseModel):
    system_prompts: list[str] = Field(..., min_items=1, max_items=5)
    temperature: float = Field(..., ge=0.0, le=1.0)
    assist_id: str = Field(default_factory=lambda: str(uuid.uuid4()))
    context_tokens: int = Field(..., gt=0)
    
    @validator("system_prompts")
    def check_prompt_length(cls, v):
        for p in v:
            if len(p) > 2000:
                raise ValueError("Individual system prompts must not exceed 2000 characters.")
        return v

def build_inject_payload(
    system_prompts: list[str],
    temperature: float,
    assist_id: Optional[str] = None
) -> PromptMatrix:
    return PromptMatrix(
        system_prompts=system_prompts,
        temperature=temperature,
        assist_id=assist_id or str(uuid.uuid4()),
        context_tokens=sum(len(p.split()) for p in system_prompts)
    )

Step 3: Validate Schema Against Gateway Constraints

The payload must pass strict validation against the fetched gateway limits. This step prevents injection failures caused by context overflow or out-of-range temperature values.

def validate_payload_against_constraints(payload: PromptMatrix, constraints: dict) -> bool:
    if payload.context_tokens > constraints["max_context_tokens"]:
        raise ValueError(
            f"Context window overflow: {payload.context_tokens} tokens exceeds gateway limit of {constraints['max_context_tokens']}."
        )
    if not (constraints["min_temperature"] <= payload.temperature <= constraints["max_temperature"]):
        raise ValueError(
            f"Temperature out of range: {payload.temperature} is outside [{constraints['min_temperature']}, {constraints['max_temperature']}]."
        )
    return True

Step 4: Serialize to Atomic Binary Frame & Inject via WebSocket

Genesys Cloud streaming endpoints accept authenticated WebSocket connections. This code serializes the validated payload into a binary frame, verifies the format, and transmits it atomically.

import websockets
import json
import struct
import asyncio

async def inject_prompt_via_websocket(
    ws_uri: str,
    token: str,
    payload: PromptMatrix,
    moderation_webhook_url: str
) -> dict:
    headers = {
        "Authorization": f"Bearer {token}",
        "Sec-WebSocket-Protocol": "genesys-streaming-v2"
    }
    
    async with websockets.connect(ws_uri, extra_headers=headers) as ws:
        # Serialize to binary: [4-byte length][JSON payload]
        json_bytes = json.dumps(payload.dict()).encode("utf-8")
        binary_frame = struct.pack(">I", len(json_bytes)) + json_bytes
        
        try:
            await ws.send(binary_frame)
            response_bytes = await asyncio.wait_for(ws.recv(), timeout=10.0)
            
            # Verify response format
            if not isinstance(response_bytes, bytes) or len(response_bytes) < 4:
                raise ValueError("Invalid binary response frame from gateway.")
            
            payload_len = struct.unpack(">I", response_bytes[:4])[0]
            response_json = json.loads(response_bytes[4:4+payload_len].decode("utf-8"))
            
            # Trigger external moderation sync
            await asyncio.create_task(
                sync_moderation_webhook(moderation_webhook_url, payload, response_json)
            )
            
            return response_json
        except websockets.exceptions.ConnectionClosed as e:
            logger.error("WebSocket closed during injection: %s", str(e))
            raise
        except asyncio.TimeoutError:
            logger.error("Gateway did not respond within timeout.")
            raise

Step 5: Safety Filtering, Webhook Sync, Latency Tracking & Audit Logging

This step implements jailbreak pattern detection, output format verification, webhook synchronization, latency measurement, and audit log generation.

import re
import aiohttp
from datetime import datetime, timezone

JAILBREAK_PATTERNS = [
    r"ignore\s+previous\s+instructions",
    r"act\s+as\s+if\s+you\s+are\s+not\s+an\s+ai",
    r"bypass\s+safety\s+filters",
    r"system\s+override"
]

def check_jailbreak_patterns(prompts: list[str]) -> bool:
    combined = " ".join(prompts).lower()
    for pattern in JAILBREAK_PATTERNS:
        if re.search(pattern, combined):
            return True
    return False

async def sync_moderation_webhook(webhook_url: str, payload: PromptMatrix, response: dict) -> None:
    moderation_payload = {
        "assist_id": payload.assist_id,
        "timestamp": datetime.now(timezone.utc).isoformat(),
        "prompt_hash": hashlib.sha256(" ".join(payload.system_prompts).encode()).hexdigest(),
        "gateway_response": response,
        "status": "submitted"
    }
    async with aiohttp.ClientSession() as session:
        try:
            async with session.post(webhook_url, json=moderation_payload, timeout=5.0) as resp:
                resp.raise_for_status()
        except Exception as e:
            logger.warning("Moderation webhook sync failed: %s", str(e))

def verify_output_format(response: dict) -> bool:
    required_keys = {"status", "assist_id", "suggestions"}
    if not required_keys.issubset(response.keys()):
        return False
    if not isinstance(response.get("suggestions"), list):
        return False
    return True

def generate_audit_log(payload: PromptMatrix, response: dict, latency_ms: float, accepted: bool) -> dict:
    return {
        "audit_id": str(uuid.uuid4()),
        "timestamp": datetime.now(timezone.utc).isoformat(),
        "assist_id": payload.assist_id,
        "temperature": payload.temperature,
        "context_tokens": payload.context_tokens,
        "latency_ms": latency_ms,
        "accepted": accepted,
        "gateway_response_status": response.get("status"),
        "jailbreak_detected": check_jailbreak_patterns(payload.system_prompts),
        "output_format_valid": verify_output_format(response)
    }

Complete Working Example

The following script integrates all components into a single executable module. It handles authentication, constraint validation, binary frame injection, safety filtering, latency tracking, and audit logging.

import asyncio
import logging
import hashlib
import uuid
import sys
from datetime import datetime, timezone

# Import all components from previous sections
# (In production, split into modules)
# from auth import GenesysTokenManager
# from gateway import fetch_gateway_constraints
# from payload import build_inject_payload, PromptMatrix, validate_payload_against_constraints
# from websocket_inject import inject_prompt_via_websocket
# from safety import check_jailbreak_patterns, sync_moderation_webhook, verify_output_format, generate_audit_log

logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
logger = logging.getLogger("prompt_injector")

async def run_prompt_injection_pipeline(
    client_id: str,
    client_secret: str,
    region: str,
    gateway_id: str,
    ws_uri: str,
    system_prompts: list[str],
    temperature: float,
    moderation_webhook_url: str
) -> None:
    token_manager = GenesysTokenManager(client_id, client_secret, region)
    
    # Step 1: Fetch constraints
    logger.info("Fetching gateway constraints for %s", gateway_id)
    constraints = await fetch_gateway_constraints(region, gateway_id, token_manager)
    
    # Step 2: Build payload
    logger.info("Constructing prompt matrix with temperature %.2f", temperature)
    payload = build_inject_payload(system_prompts, temperature)
    
    # Step 3: Validate against constraints
    logger.info("Validating payload against gateway limits")
    try:
        validate_payload_against_constraints(payload, constraints)
    except ValueError as e:
        logger.error("Validation failed: %s", str(e))
        return
    
    # Safety pre-check
    if check_jailbreak_patterns(system_prompts):
        logger.error("Jailbreak pattern detected. Injection aborted.")
        return
    
    # Step 4: Inject via WebSocket
    logger.info("Initiating WebSocket injection to %s", ws_uri)
    token = await token_manager.get_token()
    start_time = datetime.now(timezone.utc)
    
    try:
        response = await inject_prompt_via_websocket(ws_uri, token, payload, moderation_webhook_url)
        end_time = datetime.now(timezone.utc)
        latency_ms = (end_time - start_time).total_seconds() * 1000
        
        # Step 5: Verify & Audit
        format_valid = verify_output_format(response)
        accepted = response.get("status") == "accepted" and format_valid
        
        audit = generate_audit_log(payload, response, latency_ms, accepted)
        logger.info("Audit log generated: %s", json.dumps(audit, indent=2))
        
        if not accepted:
            logger.warning("Prompt injection rejected by gateway: %s", response.get("error", "unknown"))
        else:
            logger.info("Prompt injection accepted. Latency: %.2f ms", latency_ms)
            
    except Exception as e:
        logger.error("Injection pipeline failed: %s", str(e))
        raise

if __name__ == "__main__":
    # Configuration
    CONFIG = {
        "client_id": "YOUR_CLIENT_ID",
        "client_secret": "YOUR_CLIENT_SECRET",
        "region": "usw2",
        "gateway_id": "YOUR_GATEWAY_ID",
        "ws_uri": "wss://api.usw2.genesyscloud.com/v2/analytics/events/stream",
        "system_prompts": [
            "You are a customer service assistant specialized in billing inquiries.",
            "Always verify account ownership before sharing details.",
            "Maintain a professional and empathetic tone."
        ],
        "temperature": 0.7,
        "moderation_webhook_url": "https://your-moderation-service.example.com/api/v1/sync"
    }
    
    asyncio.run(run_prompt_injection_pipeline(**CONFIG))

Common Errors & Debugging

Error: 401 Unauthorized on WebSocket Handshake

• Cause: The Bearer token is expired, malformed, or missing the agentassist:prompt:write scope.
• Fix: Verify the token manager refreshes the token before injection. Ensure the OAuth client has the correct scopes assigned in the Genesys Cloud admin console.
• Code Fix: Add explicit scope validation in the token manager and log the exact Authorization header before connection.

Error: 429 Too Many Requests During Token Fetch or Injection

• Cause: Exceeding Genesys Cloud rate limits for OAuth or streaming endpoints.
• Fix: Implement exponential backoff. The provided token manager already retries on 429. For WebSocket injection, add a delay between consecutive inject calls.
• Code Fix: Wrap inject_prompt_via_websocket in a retry decorator with jittered delays.

Error: Context Window Overflow Validation Failure

• Cause: The combined token count of system prompts exceeds the gateway’s max_context_tokens constraint.
• Fix: Truncate or compress prompts before building the payload. Use a tokenization library to count accurately.
• Code Fix: Replace len(p.split()) with tiktoken.encoding_for_model("gpt-4").encode(p) for precise token counting.

Error: Invalid Binary Response Frame

• Cause: The gateway returned text instead of the expected [4-byte length][JSON] binary structure, or the connection dropped mid-stream.
• Fix: Verify the WebSocket protocol version matches the gateway specification. Add a heartbeat ping/pong mechanism to maintain connection stability.
• Code Fix: Implement websockets.ping() every 30 seconds and validate response byte length before unpacking.

Error: Jailbreak Pattern Detection False Positive

• Cause: Legitimate system prompts contain substrings matching the regex patterns.
• Fix: Use a dedicated LLM moderation endpoint or a more sophisticated pattern matching library with context awareness.
• Code Fix: Replace regex with a call to an external safety classifier API before payload construction.

Official References

• Genesys Cloud Conversations API Reference
• Genesys Cloud OAuth 2.0 Documentation
• Genesys Cloud Python SDK Repository
• WebSocket Streaming Protocol Specification