mkdir security-audit && cd security-audit
npm init -y
npm install deepsec

// audit.config.mjs
import { createAuditWorkspace } from "deepsec/config";

export default createAuditWorkspace({
  targets: [
    {
      identifier: "core-api",
      sourcePath: "../src",
      outputDir: "./reports/core-api",
    },
    {
      identifier: "admin-panel",
      sourcePath: "../admin",
      outputDir: "./reports/admin-panel",
    },
  ],
  pipeline: {
    enableRegexPreFilter: true,
    enableRevalidation: true,
    exportFormat: "markdown",
  },
});

es architectural boundaries. Keep this under 100 lines. Overloading it with implementation details dilutes the model's attention window.

# core-api Context

## Scope
Multi-tenant SaaS backend handling subscription billing and user data export.

## Auth Primitives
- `verifySession(req)`: Middleware that validates JWT and attaches `req.user`
- `enforceTenant(query, tenantId)`: Wraps all Prisma queries to prevent cross-tenant data leakage
- `requireRole(role)`: Route guard checking `req.user.role` against allowed roles

## Threat Boundaries
- Public routes: `/health`, `/webhooks/stripe`, `/marketing/*`
- Tenant-scoped data access is mandatory for all `/api/v1/*` endpoints
- Session tokens are stored in HTTP-only cookies; never logged

## Known False Positives
- `src/utils/logger.ts`: Intentionally logs request IDs and status codes
- `tests/fixtures/`: Contains mock credentials for integration testing

Step 4: Deploy the Routing Proxy

Configure Lynkr to expose an Anthropic-compatible endpoint. The proxy will handle complexity scoring and model selection.

// proxy-server.mjs
import { createLynkrProxy } from "lynkr/core";
import { ollamaProvider } from "lynkr/providers/ollama";
import { anthropicProvider } from "lynkr/providers/anthropic";

const proxy = createLynkrProxy({
  port: 8080,
  providers: {
    local: ollamaProvider({ model: "qwen2.5-coder:7b", baseUrl: "http://localhost:11434" }),
    midTier: anthropicProvider({ model: "claude-sonnet-4-20240620", apiKey: process.env.ANTHROPIC_KEY }),
    frontier: anthropicProvider({ model: "claude-opus-4-20240514", apiKey: process.env.ANTHROPIC_KEY }),
  },
  routing: {
    strategy: "complexity-score",
    thresholds: {
      local: { maxScore: 35 },
      midTier: { minScore: 36, maxScore: 70 },
      frontier: { minScore: 71 },
    },
    scoring: {
      weightImports: 0.3,
      weightSecurityKeywords: 0.5,
      weightFileSize: 0.2,
    },
  },
  telemetry: {
    enabled: true,
    dashboardPort: 8081,
    budgetLimit: 500, // monthly USD cap
  },
});

proxy.start();

Step 5: Connect Scanner to Proxy

Point the scanner's environment configuration to the local proxy. The scanner will believe it is communicating with a standard Anthropic endpoint, while the proxy handles routing transparently.

# .env.local
ANTHROPIC_BASE_URL=http://localhost:8080/v1
ANTHROPIC_API_KEY=proxy-routing-token

Run the audit:

npx deepsec run --workspace ./audit.config.mjs

Pitfall Guide

1. Context File Bloat

Explanation: Developers often paste entire architecture diagrams, dependency trees, or coding standards into the context file. This consumes valuable context window space and forces the model to ignore critical security rules. Fix: Limit the context file to 50-100 lines. Focus exclusively on auth primitives, threat boundaries, and known false-positive paths. Remove implementation details and style guidelines.

2. Disabling the Revalidation Pass

Explanation: AI models generate plausible but incorrect findings when evaluating complex code. Skipping the second-pass verification drastically increases false positives, making the output unusable. Fix: Always enable revalidation. The proxy routes both the initial analysis and the verification pass through the same complexity scoring logic, ensuring cost efficiency without sacrificing accuracy.

3. Misaligned Complexity Thresholds

Explanation: Default routing thresholds may classify a lightweight auth utility as "mid-tier" because it lacks heavy imports, causing it to bypass the frontier model. Fix: Tune the scoring heuristic to prioritize security keywords over file size. Add explicit override rules for files matching patterns like *auth*, *payment*, *crypto*, or *middleware* to force frontier routing regardless of score.

4. Unchecked Token Budgets

Explanation: Continuous scanning without hard limits can exhaust cloud credits during dependency updates or large PRs that touch hundreds of files. Fix: Configure the proxy's budget enforcement module. Set monthly caps, enable per-project throttling, and integrate telemetry alerts with your monitoring stack (Datadog, Prometheus, or Slack webhooks).

5. Treating AI Output as Ground Truth

Explanation: LLM-based scanners lack deep dataflow analysis and whole-program reasoning. They will miss subtle race conditions, complex deserialization chains, or framework-specific vulnerabilities. Fix: Run AI scanning alongside traditional SAST tools like Semgrep or CodeQL. Use AI for context-aware logic flaws and traditional tools for pattern-based vulnerabilities. Manually triage all CRITICAL and HIGH findings before merging.

6. Hardcoding API Credentials in CI

Explanation: Committing cloud API keys to repository configuration files exposes them to version control history and CI logs. Fix: Store credentials in your CI platform's secret manager. Inject them at runtime via environment variables. The proxy should only receive a routing token, not direct provider keys.

7. Ignoring Incremental Scan Modes

Explanation: Running full repository scans on every commit wastes resources on unchanged files. Fix: Configure the scanner to accept a --changed-files flag or integrate with Git diff hooks. Only route modified or newly added files through the AI pipeline. Archive previous findings and diff the output.

Production Bundle

Action Checklist

• Initialize isolated audit workspace and install scanner dependencies
• Deploy Ollama locally and pull a code-optimized model (qwen2.5-coder:7b)
• Configure Lynkr proxy with complexity scoring thresholds and budget limits
• Engineer a concise PROJECT_CONTEXT.md focusing on auth primitives and threat boundaries
• Point scanner environment variables to the local proxy endpoint
• Enable regex pre-filtering and revalidation in the scanner configuration
• Integrate incremental scanning with Git hooks or CI diff tools
• Set up telemetry monitoring and budget alerting for cloud model usage

Decision Matrix

Scenario	Recommended Approach	Why	Cost Impact
Pre-merge PR with <50 changed files	Hybrid Tiered Routing	Balances speed and accuracy; local models handle utilities, cloud handles logic	~$1.50 per PR
Full repository audit (quarterly)	Hybrid Tiered Routing + Incremental Baseline	Captures drift since last scan without reprocessing stable files	~$25 per audit
Compliance audit (HIPAA/SOC2)	Local-First Routing + Cloud Fallback	Keeps sensitive code on-prem; only routes anonymized snippets to cloud if needed	~$0.10 per PR
Legacy codebase with poor structure	Uniform Cloud AI (Opus)	High noise and unclear boundaries require frontier reasoning to avoid false negatives	~$600 per scan
CI pipeline with strict budget caps	Local Ollama Only	Eliminates cloud spend; acceptable for internal tools with lower risk tolerance	~$0 per scan

Configuration Template

Copy this template to bootstrap a production-ready scanning workspace. Adjust thresholds and context paths to match your repository structure.

// audit.config.mjs
import { createAuditWorkspace } from "deepsec/config";

export default createAuditWorkspace({
  targets: [
    {
      identifier: "production-api",
      sourcePath: "../src",
      outputDir: "./reports/production-api",
      contextFile: "./context/production-api.md",
    },
  ],
  pipeline: {
    enableRegexPreFilter: true,
    enableRevalidation: true,
    exportFormat: "markdown",
    incrementalMode: true,
  },
  routing: {
    proxyUrl: "http://localhost:8080/v1",
    routingToken: "proxy-routing-token",
    fallbackModel: "claude-sonnet-4-20240620",
  },
});

# context/production-api.md
## Scope
Production API handling user authentication, subscription billing, and data export.

## Auth Primitives
- `verifySession(req)`: Validates JWT, attaches `req.user`
- `enforceTenant(query, tenantId)`: Wraps all database queries
- `requireRole(role)`: Route guard for RBAC

## Threat Boundaries
- Public: `/health`, `/webhooks/*`, `/public/*`
- Tenant isolation mandatory for all `/api/*`
- Session tokens in HTTP-only cookies; never logged

## False Positives
- `src/utils/logger.ts`: Logs request IDs only
- `tests/mocks/`: Contains test credentials

Quick Start Guide

1. Install Dependencies: Run npm install deepsec in a new .audit/ directory. Pull qwen2.5-coder:7b via Ollama.
2. Start Proxy: Launch Lynkr with the provided configuration template. Verify telemetry dashboard at localhost:8081.
3. Configure Context: Create context/PROJECT.md with auth primitives, threat boundaries, and false-positive paths. Keep it under 100 lines.
4. Run Initial Scan: Execute npx deepsec run --workspace ./audit.config.mjs. Monitor proxy telemetry to verify routing distribution.
5. Integrate CI: Add a Git hook or CI step that runs deepsec run --changed-files on pull requests. Configure budget alerts in the proxy dashboard.