Skill

Harden web applications with security-first development

Security-first web app practices: threat modeling, OWASP prevention patterns, secrets hygiene, and LLM-specific hardening.

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91
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Updated 22 days ago
Version 0.6.3

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Why it matters

Apply threat modeling and OWASP Top 10 prevention patterns to secure web applications by validating inputs, protecting sensitive data, and enforcing authorization at every trust boundary.

Outcomes

What it gets done

01

Map trust boundaries and run STRIDE threat analysis on user input, APIs, and LLM output

02

Prevent injection attacks with parameterized queries and sanitized output encoding

03

Implement secure authentication with bcrypt password hashing and httpOnly session cookies

04

Block SSRF attacks by allowlisting hosts and rejecting private IP addresses

Install

Add it to your toolbox

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/ag-security-and-hardening | bash

Overview

Security and Hardening

Gives code-level security prevention patterns for web apps and LLM features - threat modeling with STRIDE, OWASP Top 10 mitigations including an SSRF allowlist function, secrets and supply-chain hygiene, and OWASP LLM Top 10 hardening. Use it whenever code accepts user input, handles auth or sensitive data, calls external services, or integrates an LLM. Treat every external input, including model output, as hostile.

What it does

Gives security-first development practices for web applications: treat every external input as hostile, every secret as sacred, and every authorization check as mandatory, since security is a constraint on every line of code touching user data, authentication, or external systems, not a separate phase. It works through a threat-model-first process, a three-tier rule system for what to always do, ask approval for, and never do, concrete prevention patterns mapped to the OWASP Top 10 including server-side request forgery with a TOCTOU caveat, input-validation and file-upload patterns, dependency-audit triage and supply-chain hygiene, rate limiting, secrets management, and a full section specifically for securing AI and LLM features mapped to the OWASP Top 10 for LLM Applications.

When to use - and when NOT to

Use it when building anything that accepts user input, implementing authentication or authorization, storing or transmitting sensitive data, integrating with external APIs or services, adding file uploads, webhooks, or callbacks, or handling payment or PII data. Before hardening anything, spend five minutes threat-modeling like an attacker rather than bolting controls on as guesses: map every trust boundary where untrusted data crosses in (HTTP requests, form fields, file uploads, webhooks, third-party APIs, message queues, and LLM output all count), name the assets actually worth stealing or breaking (credentials, PII, payment data, admin actions, money movement), run the STRIDE lens across each boundary (spoofing, tampering, repudiation, information disclosure, denial of service, elevation of privilege, each with its own typical mitigation), and write an abuse case next to every use case by asking how the feature could be misused, then make that the first test. Not being able to name a feature's trust boundaries means it isn't ready to be secured yet - this maps to OWASP's Insecure Design category, since most breaches start in design rather than code.

Inputs and outputs

A three-tier rule system governs day-to-day work. Always do, no exceptions: validate all external input at the system boundary, parameterize every database query rather than concatenating user input into SQL, encode output through framework auto-escaping to prevent XSS, use HTTPS everywhere, hash passwords with bcrypt, scrypt, or argon2 rather than storing plaintext, set security headers, use httpOnly and secure and sameSite session cookies, and run a dependency audit before every release. Ask first, requiring human approval: new authentication flows or auth-logic changes, storing new categories of sensitive data, new external service integrations, CORS changes, file upload handlers, rate-limiting changes, and granting elevated permissions or roles. Never do: commit secrets to version control, log sensitive data, trust client-side validation as a security boundary, disable security headers for convenience, use eval or innerHTML with user-provided data, store session tokens in client-accessible storage like localStorage, or expose stack traces or internal error details to users.

Integrations

Concrete prevention patterns exist for each major OWASP category: injection is prevented by parameterized queries or an ORM instead of string-concatenated SQL; broken authentication is prevented by password hashing at a real cost factor plus httpOnly and secure and sameSite session cookies read from environment configuration; XSS is prevented by framework auto-escaping or, when raw HTML must render, sanitizing it first rather than assigning straight to innerHTML; broken access control is prevented by checking resource ownership on every request, not just authentication; security misconfiguration is prevented by a security-header middleware plus a real Content-Security-Policy and a CORS policy restricted to known origins; sensitive-data exposure is prevented by stripping password hashes and reset tokens from any API response and reading secrets from environment variables that fail loudly if missing; and server-side request forgery, which is any case where the server fetches a URL the user influenced such as a webhook, an import-from-URL feature, an image proxy, or a link preview, is prevented by allowlisting scheme and host, resolving every DNS record and rejecting if any resolved address is a private or reserved range rather than a public unicast address, and forbidding redirects:

async function assertSafeUrl(raw: string): Promise<URL> {
  const url = new URL(raw);
  if (url.protocol !== 'https:') throw new Error('https only');
  if (!ALLOWED_HOSTS.has(url.hostname)) throw new Error('host not allowed');
  const addrs = await lookup(url.hostname, { all: true });
  if (addrs.some((a) => ipaddr.parse(a.address).range() !== 'unicast')) {
    throw new Error('private/reserved IP');
  }
  return url;
}

that unicast check alone covers loopback, link-local cloud-metadata addresses, private, and unique-local ranges across both IPv4 and IPv6, with an explicit caveat that this still has a TOCTOU gap since a short-TTL DNS record can rebind between validation and connection, so a high-risk surface should resolve once and connect to the pinned address or sit behind a dedicated filtering proxy. Input validation is enforced with a schema library validating at the route handler before any business logic runs, and file uploads are restricted by allowed MIME type and a maximum size, with a note that the file extension alone should never be trusted for anything security-critical. Triaging a dependency-audit report follows a decision tree by severity and reachability: critical or high findings get fixed immediately if the vulnerable code path is actually reachable, or fixed soon if it's a dev-only or unused path; moderate findings get fixed in the next release cycle if reachable in production or tracked in the backlog otherwise; and low findings just get tracked through regular dependency updates, with any deferred fix documented alongside its reasoning and a review date. Supply-chain hygiene goes beyond what an audit catches: commit the lockfile and install with a reproducible, frozen install command in CI rather than a mutable one, review every new dependency's maintenance and popularity before adding it since every dependency is attack surface, treat postinstall scripts in unfamiliar packages with suspicion since they run arbitrary code at install time, and watch for typosquatted package names that look almost identical to a popular one. Rate limiting applies a general window-and-max policy across the whole API and a stricter, tighter policy specifically on authentication endpoints. Secrets management keeps a committed example env file with placeholder values separate from real, gitignored env files, checks a staged diff for password, secret, and token-shaped strings before every commit, and treats any secret that ever reaches a remote as compromised the instant it's committed, rotating and reissuing the credential first and only then purging it from history, since deleting the line or rewriting history alone is never enough. Securing AI and LLM features maps to its own OWASP Top 10 for LLM Applications: treat all model output as untrusted input rather than ever passing it straight into eval, SQL, a shell, innerHTML, or a file path; assume prompts can be hijacked by untrusted text anywhere in the context window, so permissions get enforced in code rather than in the system prompt; keep secrets and other users' data out of any prompt, since anything in context can be echoed back; constrain tool and agent permissions to the minimum needed and require confirmation for destructive or irreversible actions; bound token, request-rate, and recursion-depth consumption so a crafted input can't run up cost or hang the system; and for retrieval-augmented generation specifically, treat the vector store itself as a trust boundary, partitioning embeddings per tenant and validating documents before indexing so poisoned content can't steer answers. A full security-review checklist, a table of common security rationalizations paired with why each one is wrong, and a list of concrete red flags to scan for round out the reference, alongside a final verification checklist to run after implementing any security-relevant code.

Who it's for

Developers building anything that touches user input, authentication, sensitive data, external integrations, or LLM features who want concrete, code-level prevention patterns and a threat-modeling process rather than vague security advice, plus a checklist to verify the work afterward.

Source README

Security and Hardening

Overview

Security-first development practices for web applications. Treat every external input as hostile, every secret as sacred, and every authorization check as mandatory. Security isn't a phase - it's a constraint on every line of code that touches user data, authentication, or external systems.

When to Use

  • Building anything that accepts user input
  • Implementing authentication or authorization
  • Storing or transmitting sensitive data
  • Integrating with external APIs or services
  • Adding file uploads, webhooks, or callbacks
  • Handling payment or PII data

Process: Threat Model First

Controls bolted on without a threat model are guesses. Before hardening, spend five minutes thinking like an attacker:

  1. Map the trust boundaries. Where does untrusted data cross into your system? HTTP requests, form fields, file uploads, webhooks, third-party APIs, message queues, and LLM output. Every boundary is attack surface.
  2. Name the assets. What's worth stealing or breaking? Credentials, PII, payment data, admin actions, money movement.
  3. Run STRIDE over each boundary - a quick lens, not a ceremony:
Threat Ask Typical mitigation
Spoofing Can someone impersonate a user/service? Authentication, signature verification
Tampering Can data be altered in transit or at rest? Integrity checks, parameterized queries, HTTPS
Repudiation Can an action be denied later? Audit logging of security events
Information disclosure Can data leak? Encryption, field allowlists, generic errors
Denial of service Can it be overwhelmed? Rate limiting, input size caps, timeouts
Elevation of privilege Can a user gain rights they shouldn't? Authorization checks, least privilege
  1. Write abuse cases next to use cases. For each feature, ask "how would I misuse this?" - then make that your first test.

If you can't name the trust boundaries for a feature, you're not ready to secure it. This is OWASP A04: Insecure Design - most breaches begin in design, not code.

The Three-Tier Boundary System

Always Do (No Exceptions)

  • Validate all external input at the system boundary (API routes, form handlers)
  • Parameterize all database queries - never concatenate user input into SQL
  • Encode output to prevent XSS (use framework auto-escaping, don't bypass it)
  • Use HTTPS for all external communication
  • Hash passwords with bcrypt/scrypt/argon2 (never store plaintext)
  • Set security headers (CSP, HSTS, X-Frame-Options, X-Content-Type-Options)
  • Use httpOnly, secure, sameSite cookies for sessions
  • Run npm audit (or equivalent) before every release

Ask First (Requires Human Approval)

  • Adding new authentication flows or changing auth logic
  • Storing new categories of sensitive data (PII, payment info)
  • Adding new external service integrations
  • Changing CORS configuration
  • Adding file upload handlers
  • Modifying rate limiting or throttling
  • Granting elevated permissions or roles

Never Do

  • Never commit secrets to version control (API keys, passwords, tokens)
  • Never log sensitive data (passwords, tokens, full credit card numbers)
  • Never trust client-side validation as a security boundary
  • Never disable security headers for convenience
  • Never use eval() or innerHTML with user-provided data
  • Never store sessions in client-accessible storage (localStorage for auth tokens)
  • Never expose stack traces or internal error details to users

OWASP Top 10 Prevention Patterns

These are prevention patterns, not a ranking. For the 2021 ordering, see the quick-reference table in references/security-checklist.md.

Injection (SQL, NoSQL, OS Command)

// BAD: SQL injection via string concatenation
const query = `SELECT * FROM users WHERE id = '${userId}'`;

// GOOD: Parameterized query
const user = await db.query('SELECT * FROM users WHERE id = $1', [userId]);

// GOOD: ORM with parameterized input
const user = await prisma.user.findUnique({ where: { id: userId } });

Broken Authentication

// Password hashing
import { hash, compare } from 'bcrypt';

const SALT_ROUNDS = 12;
const hashedPassword = await hash(plaintext, SALT_ROUNDS);
const isValid = await compare(plaintext, hashedPassword);

// Session management
app.use(session({
  secret: process.env.SESSION_SECRET,  // From environment, not code
  resave: false,
  saveUninitialized: false,
  cookie: {
    httpOnly: true,     // Not accessible via JavaScript
    secure: true,       // HTTPS only
    sameSite: 'lax',    // CSRF protection
    maxAge: 24 * 60 * 60 * 1000,  // 24 hours
  },
}));

Cross-Site Scripting (XSS)

// BAD: Rendering user input as HTML
element.innerHTML = userInput;

// GOOD: Use framework auto-escaping (React does this by default)
return <div>{userInput}</div>;

// If you MUST render HTML, sanitize first
import DOMPurify from 'dompurify';
const clean = DOMPurify.sanitize(userInput);

Broken Access Control

// Always check authorization, not just authentication
app.patch('/api/tasks/:id', authenticate, async (req, res) => {
  const task = await taskService.findById(req.params.id);

  // Check that the authenticated user owns this resource
  if (task.ownerId !== req.user.id) {
    return res.status(403).json({
      error: { code: 'FORBIDDEN', message: 'Not authorized to modify this task' }
    });
  }

  // Proceed with update
  const updated = await taskService.update(req.params.id, req.body);
  return res.json(updated);
});

Security Misconfiguration

// Security headers (use helmet for Express)
import helmet from 'helmet';
app.use(helmet());

// Content Security Policy
app.use(helmet.contentSecurityPolicy({
  directives: {
    defaultSrc: ["'self'"],
    scriptSrc: ["'self'"],
    styleSrc: ["'self'", "'unsafe-inline'"],  // Tighten if possible
    imgSrc: ["'self'", 'data:', 'https:'],
    connectSrc: ["'self'"],
  },
}));

// CORS — restrict to known origins
app.use(cors({
  origin: process.env.ALLOWED_ORIGINS?.split(',') || 'http://localhost:3000',
  credentials: true,
}));

Sensitive Data Exposure

// Never return sensitive fields in API responses
function sanitizeUser(user: UserRecord): PublicUser {
  const { passwordHash, resetToken, ...publicFields } = user;
  return publicFields;
}

// Use environment variables for secrets
const API_KEY = process.env.STRIPE_API_KEY;
if (!API_KEY) throw new Error('STRIPE_API_KEY not configured');

Server-Side Request Forgery (SSRF)

Any time the server fetches a URL the user influenced - webhooks, "import from URL", image proxies, link previews - an attacker can aim it at internal services (cloud metadata, localhost, private IPs).

// BAD: fetch whatever the user gives you
await fetch(req.body.webhookUrl);

// GOOD: allowlist scheme + host, reject if ANY resolved IP is private, forbid redirects
import { lookup } from 'node:dns/promises';
import ipaddr from 'ipaddr.js';

const ALLOWED_HOSTS = new Set(['hooks.example.com']);

async function assertSafeUrl(raw: string): Promise<URL> {
  const url = new URL(raw);
  if (url.protocol !== 'https:') throw new Error('https only');
  if (!ALLOWED_HOSTS.has(url.hostname)) throw new Error('host not allowed');
  // Resolve ALL records; a single private/reserved address fails the check.
  const addrs = await lookup(url.hostname, { all: true });
  if (addrs.some((a) => ipaddr.parse(a.address).range() !== 'unicast')) {
    throw new Error('private/reserved IP');
  }
  return url;
}

await fetch(await assertSafeUrl(req.body.webhookUrl), { redirect: 'error' });

The range() !== 'unicast' check covers loopback, link-local 169.254.169.254 (cloud metadata, the #1 SSRF target), private, and unique-local ranges across IPv4 and IPv6.

Caveat - this still has a TOCTOU gap. fetch resolves DNS again after the check, so an attacker using a short-TTL record can rebind to an internal IP between validation and connection. For high-risk surfaces, resolve once and connect to the pinned IP, or put a filtering agent in front (request-filtering-agent / ssrf-req-filter).

Input Validation Patterns

Schema Validation at Boundaries

import { z } from 'zod';

const CreateTaskSchema = z.object({
  title: z.string().min(1).max(200).trim(),
  description: z.string().max(2000).optional(),
  priority: z.enum(['low', 'medium', 'high']).default('medium'),
  dueDate: z.string().datetime().optional(),
});

// Validate at the route handler
app.post('/api/tasks', async (req, res) => {
  const result = CreateTaskSchema.safeParse(req.body);
  if (!result.success) {
    return res.status(422).json({
      error: {
        code: 'VALIDATION_ERROR',
        message: 'Invalid input',
        details: result.error.flatten(),
      },
    });
  }
  // result.data is now typed and validated
  const task = await taskService.create(result.data);
  return res.status(201).json(task);
});

File Upload Safety

// Restrict file types and sizes
const ALLOWED_TYPES = ['image/jpeg', 'image/png', 'image/webp'];
const MAX_SIZE = 5 * 1024 * 1024; // 5MB

function validateUpload(file: UploadedFile) {
  if (!ALLOWED_TYPES.includes(file.mimetype)) {
    throw new ValidationError('File type not allowed');
  }
  if (file.size > MAX_SIZE) {
    throw new ValidationError('File too large (max 5MB)');
  }
  // Don't trust the file extension — check magic bytes if critical
}

Triaging npm audit Results

Not all audit findings require immediate action. Use this decision tree:

npm audit reports a vulnerability
├── Severity: critical or high
│   ├── Is the vulnerable code reachable in your app?
│   │   ├── YES --> Fix immediately (update, patch, or replace the dependency)
│   │   └── NO (dev-only dep, unused code path) --> Fix soon, but not a blocker
│   └── Is a fix available?
│       ├── YES --> Update to the patched version
│       └── NO --> Check for workarounds, consider replacing the dependency, or add to allowlist with a review date
├── Severity: moderate
│   ├── Reachable in production? --> Fix in the next release cycle
│   └── Dev-only? --> Fix when convenient, track in backlog
└── Severity: low
    └── Track and fix during regular dependency updates

Key questions:

  • Is the vulnerable function actually called in your code path?
  • Is the dependency a runtime dependency or dev-only?
  • Is the vulnerability exploitable given your deployment context (e.g., a server-side vulnerability in a client-only app)?

When you defer a fix, document the reason and set a review date.

Supply-Chain Hygiene

npm audit catches known CVEs; it won't catch a malicious or typosquatted package. Also:

  • Commit the lockfile and install with npm ci (not npm install) in CI - reproducible builds, no silent version drift.
  • Review new dependencies before adding them - maintenance, download counts, and whether they truly earn their place. Every dependency is attack surface (OWASP A06: Vulnerable Components, LLM03: Supply Chain).
  • Be wary of postinstall scripts in unfamiliar packages - they run arbitrary code at install time.
  • Watch for typosquats - cross-env vs crossenv, react-dom vs reactdom.

Rate Limiting

import rateLimit from 'express-rate-limit';

// General API rate limit
app.use('/api/', rateLimit({
  windowMs: 15 * 60 * 1000, // 15 minutes
  max: 100,                   // 100 requests per window
  standardHeaders: true,
  legacyHeaders: false,
}));

// Stricter limit for auth endpoints
app.use('/api/auth/', rateLimit({
  windowMs: 15 * 60 * 1000,
  max: 10,  // 10 attempts per 15 minutes
}));

Secrets Management

.env files:
  ├── .env.example  → Committed (template with placeholder values)
  ├── .env          → NOT committed (contains real secrets)
  └── .env.local    → NOT committed (local overrides)

.gitignore must include:
  .env
  .env.local
  .env.*.local
  *.pem
  *.key

Always check before committing:

### Check for accidentally staged secrets
git diff --cached | grep -i "password\|secret\|api_key\|token"

If a secret is ever committed, rotate it. Deleting the line or rewriting history is not enough - assume it's compromised the moment it reaches a remote. Revoke and reissue the key first, then purge it from history.

Securing AI / LLM Features

If your app calls an LLM - chatbots, summarizers, agents, RAG - it inherits a new attack surface. Map it to the OWASP Top 10 for LLM Applications (2025):

  • Treat all model output as untrusted input (LLM05: Improper Output Handling). Never pass LLM output straight into eval, SQL, a shell, innerHTML, or a file path. Validate and encode it exactly as you would raw user input.
  • Assume prompts can be hijacked (LLM01: Prompt Injection). Untrusted text in the context window - a user message, a fetched web page, a PDF - can carry instructions. The system prompt is not a security boundary; enforce permissions in code, not in the prompt.
  • Keep secrets and other users' data out of prompts (LLM02 / LLM07). Anything in the context can be echoed back. Don't put API keys, cross-tenant data, or the full system prompt where the model can repeat it.
  • Constrain tool and agent permissions (LLM06: Excessive Agency). Scope tools to the minimum, require confirmation for destructive or irreversible actions, and validate every tool argument.
  • Bound consumption (LLM10: Unbounded Consumption). Cap tokens, request rate, and loop/recursion depth so a crafted input can't run up cost or hang the system.
  • Isolate retrieval data (LLM08: Vector and Embedding Weaknesses). In RAG, treat the vector store as a trust boundary: partition embeddings per tenant so one user can't retrieve another's data, and validate documents before indexing so poisoned content can't steer answers.
// BAD: trusting model output as a command or as markup
const sql = await llm.generate(`Write SQL for: ${userQuestion}`);
await db.query(sql);                                   // arbitrary query execution
container.innerHTML = await llm.reply(userMessage);   // stored XSS, via the model

// GOOD: model output is data — parse defensively, then validate, then encode
let intent;
try {
  intent = CommandSchema.parse(JSON.parse(await llm.replyJson(userMessage)));
} catch {
  throw new ValidationError('unexpected model output'); // JSON.parse or schema failed
}
await runAllowlistedAction(intent.action, intent.params);
container.textContent = await llm.reply(userMessage);

Security Review Checklist

### Authentication
- [ ] Passwords hashed with bcrypt/scrypt/argon2 (salt rounds ≥ 12)
- [ ] Session tokens are httpOnly, secure, sameSite
- [ ] Login has rate limiting
- [ ] Password reset tokens expire

### Authorization
- [ ] Every endpoint checks user permissions
- [ ] Users can only access their own resources
- [ ] Admin actions require admin role verification

### Input
- [ ] All user input validated at the boundary
- [ ] SQL queries are parameterized
- [ ] HTML output is encoded/escaped
- [ ] Server-side URL fetches are allowlisted (no SSRF to internal services)

### Data
- [ ] No secrets in code or version control
- [ ] Sensitive fields excluded from API responses
- [ ] PII encrypted at rest (if applicable)

### Infrastructure
- [ ] Security headers configured (CSP, HSTS, etc.)
- [ ] CORS restricted to known origins
- [ ] Dependencies audited for vulnerabilities
- [ ] Error messages don't expose internals

### Supply Chain
- [ ] Lockfile committed; CI installs with `npm ci`
- [ ] New dependencies reviewed (maintenance, downloads, postinstall scripts)

### AI / LLM (if used)
- [ ] Model output treated as untrusted (no eval/SQL/innerHTML/shell)
- [ ] Secrets and other users' data kept out of prompts
- [ ] Tool/agent permissions scoped; destructive actions require confirmation

See Also

For detailed security checklists and pre-commit verification steps, see references/security-checklist.md.

Common Rationalizations

Rationalization Reality
"This is an internal tool, security doesn't matter" Internal tools get compromised. Attackers target the weakest link.
"We'll add security later" Security retrofitting is 10x harder than building it in. Add it now.
"No one would try to exploit this" Automated scanners will find it. Security by obscurity is not security.
"The framework handles security" Frameworks provide tools, not guarantees. You still need to use them correctly.
"It's just a prototype" Prototypes become production. Security habits from day one.
"Threat modeling is overkill here" Five minutes of "how would I attack this?" prevents the design flaws no control can patch later.
"It's just LLM output, it's only text" That "text" can be a SQL statement, a script tag, or a shell command. Treat it like any untrusted input.

Red Flags

  • User input passed directly to database queries, shell commands, or HTML rendering
  • Secrets in source code or commit history
  • API endpoints without authentication or authorization checks
  • Missing CORS configuration or wildcard (*) origins
  • No rate limiting on authentication endpoints
  • Stack traces or internal errors exposed to users
  • Dependencies with known critical vulnerabilities
  • Server fetches user-supplied URLs without an allowlist (SSRF)
  • LLM/model output passed into a query, the DOM, a shell, or eval
  • Secrets, PII, or the full system prompt placed inside an LLM context window

Verification

After implementing security-relevant code:

  • npm audit shows no critical or high vulnerabilities
  • No secrets in source code or git history
  • All user input validated at system boundaries
  • Authentication and authorization checked on every protected endpoint
  • Security headers present in response (check with browser DevTools)
  • Error responses don't expose internal details
  • Rate limiting active on auth endpoints
  • Server-side URL fetches validated against an allowlist (no SSRF)
  • LLM/model output validated and encoded before use (if AI features present)

Limitations

  • Use this skill only when the task clearly matches its upstream source and local project context.
  • Verify commands, generated code, dependencies, credentials, and external service behavior before applying changes.
  • Do not treat examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.

FAQ

Common questions

Discussion

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