Node.js LLM Gateway Prompt Injection Failing on Context Window Validation

Problem

The PROMPT INJECTION CONFIG keeps rejecting payloads during the Node.js gateway handshake. We’ve built a streaming WebSocket handler to push system instruction directives and template variables into the LLM endpoint. The SAFETY POLICY MATRIX validation triggers a 422 whenever the CONTEXT WINDOW LIMIT crosses the TOKEN BUDGET CONSTRAINTS. FEW-SHOT EXAMPLE SELECTION logic gets mapped directly into the request body, but the truncation algorithm drops half the variables before the payload even hits the endpoint. ADMIN UI displays queue analytics without issue. Telemetry exports to the observability platform return empty arrays for injection latency though. Token consumption rates spike constantly. Audit logs just show generic schema mismatches. Nothing aligns with the expected orchestration flow. The variable interpolation routine doesn’t account for the automatic truncation logic. External dashboards expect precise latency metrics, but the sync keeps failing.

Code

const ws = new WebSocket('wss://api-eu-west-1.genesys.cloud/v2/ai/llm/stream');
const promptPayload = {
 system: "Enforce SAFETY POLICY MATRIX rules.",
 variables: { agentQueue: "{{QUEUE_ID}}", context: "{{DIALOGUE_HISTORY}}" },
 tokenBudget: 4096,
 optimization: "few-shot-interpolation"
};
ws.on('open', () => ws.send(JSON.stringify(promptPayload)));
ws.on('message', (data) => console.log('Token accumulation:', data));

Error

{
 "error_code": 422,
 "message": "Prompt schema validation failed. Context window limit exceeded token budget constraints.",
 "details": {
 "truncated_variables": ["{{DIALOGUE_HISTORY}}"],
 "safety_policy_violation": "INJECTION RISK DETECTED",
 "telemetry_sync_status": "FAILED"
 }
}

Question

How should the variable interpolation algorithm adjust before the WebSocket handshake completes? The truncation logic seems to ignore the DYNAMIC CONTEXT MANAGEMENT flags. It’s throwing the schema mismatch every single time.

Cause: The 422 hits because the safety policy validator expects the token budget constraint inside the conversation metadata payload, not the raw WebSocket stream. When you push system directives straight through, the BYOC edge drops the payload against the strict schema. Also, the platformClient defaults to webhook:read which doesn’t cover the LLM gateway routing rules.

Solution: Wrap the context window limit in the proper event structure and bump the OAuth scope. Here’s how we patch the Lambda handler:

const { PureCloudPlatformClientV2 } = require('genesys-cloud-sdk-nodejs');
const platformClient = new PureCloudPlatformClientV2();

await platformClient.loginClientCredentials({
 clientId: process.env.GC_CLIENT_ID,
 clientSecret: process.env.GC_CLIENT_SECRET
});

// POST /api/v2/webhooks
await platformClient.WebhooksApi.postWebhooks({
 body: {
 name: 'llm-gateway-validator',
 enabled: true,
 api_version: 'v2',
 event_filters: [{ event_type: 'conversation:analyzed' }],
 request_url: process.env.WEBHOOK_ENDPOINT,
 request_body_template: JSON.stringify({
 context_window_limit: 4096,
 token_budget: 3500,
 safety_policy_matrix: 'strict'
 })
 }
});

Check the app registration. You’ll need webhook:write and routing:edit. The validator drops the 422 once the schema matches. Usually takes two deploys to clear the cache anyway.

PureCloudPlatformClientV2 actually expects that token budget nested inside the conversation object before it hits the BYOC edge. You’ll want to swap out webhook:read for ai:gateway:write in your auth grant. Here’s the exact metadata shape.

const payload = {
 conversation: {
 tokenBudget: { limit: 4096, currentUsage: 120 },
 safetyPolicy: "strict"
 },
 directives: systemInstructions
};
await platformClient.AI.postAiConversationStream({ body: payload });

Watch out for Node backpressure on the upgrade. The validator just needs that nested structure.

payload = {"conversation": {"tokenBudget": {"limit": 4096}}, "directives": sys_prompt}

It’s working with the wrapper. The docs state “The token budget constraint must be nested within the conversation metadata object for validation.” Why does the SDK reject the flat structure then.