@floatingsidewal/bulkhead-core

@floatingsidewal/bulkhead-core

Cascading content protection engine -- detects and redacts PII, secrets, prompt injection, and system prompt leakage in text before it reaches LLMs.

Part of the Bulkhead project.

Install

Then install:

npm install @floatingsidewal/bulkhead-core

Quick Start

import { createEngine } from "@floatingsidewal/bulkhead-core";

const engine = createEngine();

// Fast regex-only scan (sub-millisecond)
const result = await engine.analyze("My SSN is 123-45-6789 and key is AKIAIOSFODNN7EXAMPLE");

console.log(result.passed);      // false
console.log(result.detections);   // [{ entityType: "US_SSN", ... }, { entityType: "AWS_ACCESS_KEY", ... }]

// Scan and redact
const redacted = await engine.scan("Call me at 555-867-5309");
console.log(redacted.redactedText); // "Call me at [REDACTED-US_PHONE]"

What It Detects

Category	Coverage
PII	45+ entity types across 20+ countries (SSN, credit cards, IBAN, phone, email, medical IDs, national IDs)
Secrets	154 patterns across 13 categories (AWS, Azure, GCP, GitHub, Slack, Stripe, database credentials, private keys)
Prompt Injection	16+ patterns (role-play attacks, DAN mode, instruction override)
System Prompt Leakage	7+ patterns (prompt extraction, "repeat everything above")

All structured patterns include checksum validation where applicable (Luhn, IBAN mod-97, Verhoeff).

Configuration

import { createEngine, type BulkheadConfig } from "@floatingsidewal/bulkhead-core";

const engine = createEngine({
  enabled: true,
  debounceMs: 500,
  guards: {
    pii: { enabled: true },
    secret: { enabled: true },
    injection: { enabled: true },
    contentSafety: { enabled: false },
  },
  cascade: {
    modelEnabled: false,       // Enable BERT layer (see below)
    escalationThreshold: 0.75,
    contextSentences: 3,
    modelId: "Xenova/bert-base-NER",
  },
});

Custom Guard Composition

For fine-grained control, compose guards directly:

import { GuardrailsEngine, PiiGuard, SecretGuard } from "@floatingsidewal/bulkhead-core";

const engine = new GuardrailsEngine();
engine.addGuard(new PiiGuard());
engine.addGuard(new SecretGuard());
// Skip injection/leakage guards if not needed

BERT Layer (Optional)

The regex layer catches structured patterns. For contextual entities like names, locations, and organizations, enable the BERT layer:

npm install @huggingface/transformers

const engine = createEngine({
  // ...
  cascade: {
    modelEnabled: true,  // Enables BERT NER model
    // ...
  },
});

// Use deepScan for full cascade (regex + BERT + optional LLM)
const result = await engine.deepScan("Send the report to John Smith at Acme Corp");

// Or modelScan for regex + BERT only (no LLM)
const result = await engine.modelScan(text);

The BERT model (~29 MB) downloads on first inference and runs in a worker thread. No GPU required.

API

createEngine(config?)

Creates a configured engine from a BulkheadConfig. Returns a GuardrailsEngine.

engine.analyze(text)

Layer 1 only (regex). Sub-millisecond. Returns { passed, detections, stats }.

engine.scan(text)

Layer 1 scan with redaction. Returns { passed, detections, redactedText, stats }.

engine.modelScan(text)

Regex + BERT (Layers 1-2). Requires cascade.modelEnabled: true.

engine.deepScan(text)

Full cascade (Layers 1-3). Requires cascade configuration.

engine.dispose()

Cleanup (terminates BERT worker thread if running).

Documentation

See the full documentation for architecture details, deployment guides, and API reference.

License

MIT