Security Vulnerability Missing Rate Limiting In App.ts Explained And Addressed

Hey guys! Today, we're diving deep into a critical security vulnerability: missing rate limiting. We'll break down what this means, why it's a big deal, and how to fix it. We'll be focusing on a specific instance flagged by CodeQL in a file named app.ts, line 23, but the principles we cover apply to any application.

Understanding the Vulnerability: Missing Rate Limiting

In this section, we'll cover the following:

• Defining Rate Limiting: Rate limiting is the practice of restricting the number of requests a user can make to a server within a given time frame. It's a crucial security measure that prevents abuse and ensures fair usage of resources. Think of it like a bouncer at a club, making sure things don't get too crowded or rowdy.
• Why Rate Limiting Matters: Without rate limiting, your application becomes vulnerable to several threats, including:
  • Brute-force attacks: Attackers can repeatedly try different passwords or access codes until they find the correct one.
  • Denial-of-service (DoS) attacks: Malicious actors can flood your server with requests, overwhelming it and making it unavailable to legitimate users.
  • Resource exhaustion: Excessive requests can strain your server's resources, leading to performance degradation or even crashes.
  • Account enumeration: Attackers can try different usernames to see which ones exist, potentially leading to targeted attacks.
• OWASP A04: Insecure Design: Missing rate limiting falls under the OWASP A04: Insecure Design category. This means the vulnerability stems from a flaw in the application's architecture, rather than a simple coding error. It highlights the importance of building security into the design process from the start. Insecure Design represents missing or ineffective control design, distinct from insecure implementation. A secure design can still have implementation defects, but an insecure design cannot be fixed by a perfect implementation alone.

Diving Deeper into Insecure Design (OWASP A04)

• Definition: Insecure design isn't just about bugs in the code; it's about fundamental flaws in the architecture of your application. A secure design considers potential threats and implements controls to mitigate them. Even the most perfectly written code can be vulnerable if the underlying design is flawed.
• Common Manifestations: Let's look at some common examples of insecure design:
  • Predictable Tokens: Imagine tokens that are easy to guess, like sequential IDs or timestamps. This allows attackers to impersonate users or access restricted resources.
  • Missing Rate Limiting: The very issue we're discussing! Unlimited requests open the door to brute-force attacks and DoS attacks.
  • Business Logic Flaws: Think about loopholes in your application's logic, like allowing negative quantities in orders or transfers to oneself. These can lead to financial losses or data corruption.
  • Insufficient Threat Modeling: Failing to consider security during the design phase is a recipe for disaster. Threat modeling helps identify potential vulnerabilities before they're coded into the application.
  • No Token Expiration: Tokens that remain valid forever are a huge security risk. If a token is compromised, it can be used indefinitely.
  • Reusable Tokens: Password reset tokens, for example, should only be used once. Reusing tokens opens the door to account takeover.
• Why It Matters: Insecure design is a big deal because it's difficult and costly to fix after deployment. It often requires architectural changes, not just simple code patches. That's why proactive security measures, like threat modeling and secure design principles, are crucial. Remember, design flaws can be very costly to fix after deployment and require architectural changes rather than simple patches. Threat modeling and secure design principles must be applied before writing code.

STRIDE and Insecure Design

STRIDE is a threat modeling methodology that helps identify different types of security threats. In the context of insecure design, here's how STRIDE categories map:

• Spoofing: Predictable tokens enable impersonation, a classic spoofing attack.
• Information Disclosure: Design flaws can leak sensitive system state or data.
• Tampering: Business logic bypasses allow attackers to manipulate data or processes.

Analyzing the Vulnerable Code in app.ts

Unfortunately, the provided snippet lacks the actual code from app.ts. However, the issue description states that a route handler performing a database access is missing rate limiting. Let's imagine what this might look like and how to address it.

Hypothetical Vulnerable Code:

// Hypothetical vulnerable code in app.ts

import express, { Request, Response } from 'express';
const app = express();

app.get('/api/users/:id', async (req: Request, res: Response) => {
  const userId = req.params.id;
  // Database access without rate limiting
  const user = await db.getUser(userId);
  if (user) {
    res.json(user);
  } else {
    res.status(404).send('User not found');
  }
});

export default app;

In this example, the /api/users/:id route retrieves user data from a database. If this route is hit repeatedly, it could strain the database and potentially lead to a DoS attack. Remember, this is just a hypothetical example.

Identifying the Issue

The core issue here is the lack of rate limiting. There's no mechanism to prevent a user (or an attacker) from sending numerous requests to this endpoint within a short period. This can lead to:

• Database overload: The database might struggle to handle a large number of requests, causing slowdowns or even crashes.
• Resource exhaustion: The server itself might run out of resources (CPU, memory) trying to process all the requests.
• Denial of service: Legitimate users might be unable to access the application if the server is overwhelmed.

Implementing Rate Limiting: A Practical Guide

Alright, guys, let's get to the good stuff: how to fix this! Here's a breakdown of how to implement rate limiting in your Node.js application. We'll cover different approaches, from simple in-memory solutions to more robust options using Redis.

1. Choosing a Rate Limiting Strategy

There are several ways to implement rate limiting, each with its own trade-offs. Here are a few common strategies:

• Fixed Window: This is the simplest approach. It divides time into fixed intervals (e.g., 1 minute) and limits the number of requests within each window. However, it's susceptible to burst attacks at the edges of the window.
• Sliding Window: A more sophisticated approach that considers a rolling time window. This provides better protection against burst attacks but is more complex to implement.
• Token Bucket: This algorithm uses a "bucket" that holds a certain number of tokens. Each request consumes a token, and tokens are refilled at a নির্দিষ্ট rate. This allows for burst traffic while still enforcing an average rate limit.
• Leaky Bucket: Similar to the token bucket, but requests are processed at a fixed rate, and excess requests are either queued or dropped. This provides a smooth rate limit.

2. Using Middleware for Rate Limiting

The most common and effective way to implement rate limiting in Express.js is by using middleware. Middleware functions have access to the request and response objects, making them ideal for intercepting requests and applying rate limiting logic.

A. In-Memory Rate Limiting (for Development/Small Applications)

For smaller applications or development environments, you can use a simple in-memory rate limiting solution. This approach stores rate limiting data in the server's memory. However, it's not suitable for production environments with multiple server instances, as the rate limiting data won't be shared across instances.

// Simple in-memory rate limiting middleware
import { Request, Response, NextFunction } from 'express';

const rateLimit = (maxRequests: number, windowMs: number) => {
  const requestCounts = new Map<string, number>();
  return (req: Request, res: Response, next: NextFunction) => {
    const ip = req.ip; // Or use a more specific identifier like user ID
    const currentCount = requestCounts.get(ip) || 0;
    if (currentCount >= maxRequests) {
      return res.status(429).send('Too many requests, please try again later.');
    }
    requestCounts.set(ip, currentCount + 1);
    setTimeout(() => {
      requestCounts.delete(ip);
    }, windowMs);
    next();
  };
};

// Example usage
app.get('/api/users/:id', rateLimit(10, 60000), async (req: Request, res: Response) => {
  // ... your route logic ...
});

This middleware limits each IP address to 10 requests per minute. Let's break it down:

• rateLimit(maxRequests, windowMs): This function returns the middleware.
  • maxRequests: The maximum number of requests allowed within the time window.
  • windowMs: The time window in milliseconds.
• requestCounts: A Map stores the number of requests from each IP address.
• Inside the middleware function:
  • It gets the IP address from the request object.
  • It retrieves the current request count for that IP.
  • If the count exceeds maxRequests, it returns a 429 Too Many Requests error.
  • Otherwise, it increments the count and sets a timeout to delete the count after the time window.
  • next(): This calls the next middleware function in the chain (your route handler).

B. Using express-rate-limit with In-Memory Store

For a slightly more robust in-memory solution, you can use the express-rate-limit middleware:

// Using express-rate-limit with in-memory store
import rateLimit from 'express-rate-limit';

const limiter = rateLimit({
  windowMs: 60 * 1000, // 1 minute
  max: 10, // Limit each IP to 10 requests per windowMs
  message:
    'Too many requests from this IP, please try again after 1 minute',
  standardHeaders: true, // Return rate limit info in the `RateLimit-*` headers
  legacyHeaders: false, // Disable the `X-RateLimit-*` headers
});

// Apply the rate limiting middleware to all requests
app.use(limiter);

// Or apply to a specific route
app.get('/api/users/:id', limiter, async (req: Request, res: Response) => {
  // ... your route logic ...
});

express-rate-limit provides a more configurable and feature-rich rate limiting solution. Key options include:

• windowMs: The time window in milliseconds.
• max: The maximum number of requests allowed within the window.
• message: The message sent to the client when the rate limit is exceeded.
• standardHeaders: Whether to include rate limit information in the RateLimit-* headers.
• legacyHeaders: Whether to include the legacy X-RateLimit-* headers.

C. Using express-rate-limit with Redis Store (for Production)

For production environments, you'll want a rate limiting solution that can handle multiple server instances and persist data. Redis is a popular in-memory data store that's well-suited for this purpose. Here's how to use express-rate-limit with a Redis store:

// Using express-rate-limit with Redis store
import rateLimit from 'express-rate-limit';
import RedisStore from 'rate-limit-redis';
import { createClient } from 'redis';

const redisClient = createClient({
  // configure your redis client
  socket: {},
});

redisClient.connect().catch(console.error);

const limiter = rateLimit({
  store: new RedisStore({\n    sendCommand: (...args: string[]) => redisClient.sendCommand(args),
  }),
  windowMs: 60 * 1000, // 1 minute
  max: 100, // Limit each IP to 100 requests per windowMs
  message:
    'Too many requests from this IP, please try again after 1 minute',
  standardHeaders: true, // Return rate limit info in the `RateLimit-*` headers
  legacyHeaders: false, // Disable the `X-RateLimit-*` headers
});

// Apply the rate limiting middleware to all requests
app.use(limiter);

// Or apply to a specific route
app.get('/api/users/:id', limiter, async (req: Request, res: Response) => {
  // ... your route logic ...
});

This example uses the rate-limit-redis package to store rate limiting data in Redis. Key steps include:

1. Import the necessary packages.
2. Create a Redis client.
3. Create a RedisStore instance, passing in the Redis client.
4. Configure the express-rate-limit middleware, using the RedisStore.
5. Apply the middleware to your application or specific routes.

3. Applying Rate Limiting to Specific Routes

While you can apply rate limiting to your entire application, it's often more efficient to apply it to specific routes that are more vulnerable or resource-intensive. This minimizes the impact on legitimate users while still protecting your application.

As shown in the examples above, you can apply the middleware to a specific route by passing it as an argument to the app.get(), app.post(), etc., methods.

4. Customizing Rate Limiting Logic

express-rate-limit offers various options for customizing the rate limiting logic. Here are a few examples:

• Key Generator: By default, express-rate-limit uses the client's IP address as the key for rate limiting. You can customize this by providing a keyGenerator function:

  // Custom key generator (e.g., use user ID if authenticated)
  const limiter = rateLimit({
    // ... other options ...
    keyGenerator: (req: Request, res: Response) => {
      return req.user ? req.user.id : req.ip;
    },
  });
  

• Skip: You can conditionally skip rate limiting for certain requests using the skip option:

  // Skip rate limiting for admin users
  const limiter = rateLimit({
    // ... other options ...
    skip: (req: Request, res: Response) => {
      return req.user && req.user.isAdmin;
    },
  });
  

• Store: As we saw with Redis, you can use different stores to persist rate limiting data. express-rate-limit supports various stores, including in-memory, Redis, Memcached, and more.

5. Testing Your Rate Limiting Implementation

After implementing rate limiting, it's crucial to test it thoroughly to ensure it's working as expected. Here are a few ways to test your implementation:

• Manual Testing: Use tools like curl or Postman to send multiple requests to your API and verify that the rate limit is enforced.
• Integration Tests: Write automated tests that simulate a high volume of requests and assert that the server returns the correct status codes (e.g., 429).
• Load Testing: Use tools like Apache JMeter or k6 to simulate realistic traffic patterns and measure the impact of rate limiting on your application's performance.

6. Monitoring Rate Limiting Metrics

To ensure your rate limiting implementation is effective, it's essential to monitor key metrics, such as:

• Number of requests rate limited: This indicates how often the rate limit is being triggered.
• Average response time: This helps you identify any performance impact caused by rate limiting.
• Error rates: This can indicate issues with your rate limiting configuration or potential attacks.

You can use monitoring tools like Prometheus, Grafana, or Datadog to track these metrics.

Remediation Steps for app.ts (Line 23)

Given the information, here's a general remediation plan for the vulnerability in app.ts:

1. Install express-rate-limit and redis (if using Redis):

   npm install express-rate-limit redis rate-limit-redis --save
   

2. Implement Rate Limiting Middleware: Choose a rate limiting strategy and implement the appropriate middleware (in-memory or Redis-backed).

3. Apply Middleware to the Vulnerable Route: Apply the rate limiting middleware to the specific route handler in app.ts (line 23) that performs database access or other resource-intensive operations.

4. Configure Rate Limit Thresholds: Set appropriate rate limit thresholds based on your application's requirements and resource capacity.

5. Test the Implementation: Thoroughly test the rate limiting implementation to ensure it's working as expected and doesn't negatively impact legitimate users.

6. Monitor Performance: Monitor the performance of your application after implementing rate limiting to identify any potential issues.

Example Code (Applying Rate Limiting to Hypothetical Code)

// app.ts (with rate limiting)

import express, { Request, Response } from 'express';
import rateLimit from 'express-rate-limit';
import RedisStore from 'rate-limit-redis';
import { createClient } from 'redis';

const app = express();

// Redis setup (if using Redis)
const redisClient = createClient({
  // configure your redis client
  socket: {},
});

redisClient.connect().catch(console.error);

// Rate limiting middleware
const limiter = rateLimit({
  store: new RedisStore({\n    sendCommand: (...args: string[]) => redisClient.sendCommand(args),
  }),
  windowMs: 60 * 1000, // 1 minute
  max: 10, // Limit each IP to 10 requests per windowMs
  message:
    'Too many requests from this IP, please try again after 1 minute',
  standardHeaders: true, // Return rate limit info in the `RateLimit-*` headers
  legacyHeaders: false, // Disable the `X-RateLimit-*` headers
});

// Apply rate limiting to the /api/users/:id route
app.get('/api/users/:id', limiter, async (req: Request, res: Response) => {
  const userId = req.params.id;
  // Database access with rate limiting
  const user = await db.getUser(userId);
  if (user) {
    res.json(user);
  } else {
    res.status(404).send('User not found');
  }
});

export default app;

This example demonstrates how to apply the rate limiting middleware to the /api/users/:id route. Remember to adapt the code to your specific application and database setup.

Additional Security Considerations

Rate limiting is a crucial security measure, but it's not a silver bullet. Here are some additional security considerations to keep in mind:

• Input Validation: Always validate user input to prevent injection attacks and other vulnerabilities.
• Authentication and Authorization: Implement proper authentication and authorization mechanisms to ensure only authorized users can access your resources.
• Monitoring and Logging: Monitor your application for suspicious activity and log security events to help detect and respond to attacks.
• Regular Security Audits: Conduct regular security audits to identify and address potential vulnerabilities.

Maintainability Considerations: Fitness Functions

To ensure your application remains secure and maintainable over time, consider implementing fitness functions. Fitness functions are automated tests that measure architectural characteristics and fail if thresholds are exceeded. In the context of rate limiting, you could create fitness functions that:

• Verify that rate limiting is enabled for critical routes.
• Check that rate limit thresholds are set appropriately.
• Ensure that the rate limiting implementation is performing as expected.

Here are some fitness functions:

FITNESS FUNCTIONS

• Fitness Functions are automated, objective quality gates that continuously validate architectural characteristics. They prevent technical debt by failing builds when code degrades beyond acceptable thresholds.

🎯 What are Fitness Functions?

• Definition: Executable tests that measure architectural quality metrics (complexity, coverage, performance) and fail if thresholds are exceeded. Think "unit tests for architecture."

Common Fitness Function Types

• Complexity: Cyclomatic complexity per function (threshold: ≤10)
• Test Coverage: Line, branch, and statement coverage (threshold: ≥80%)
• Performance: p95 latency for critical endpoints (threshold: <200ms)
• Dependency Freshness: Age of dependencies (threshold: ≤90 days)
• Security: High/critical vulnerabilities (threshold: 0)

Why They Matter

• Without fitness functions, code quality degrades silently over time (architectural erosion). Manual code reviews can't catch every violation.

Maps to OWASP

• Supports: All OWASP categories by enforcing quality standards
• Primary: A06 - Vulnerable Components (dependency freshness)
• Secondary: A04 - Insecure Design (complexity reduces attack surface)

🤖 AI Prompt #1: Identify Where to Apply Fitness Functions

Role: You are an Evolutionary Architecture engineer analyzing a codebase to determine which fitness functions would provide the most value.

Context: I have the following project:

PASTE YOUR PROJECT DETAILS HERE

Example:

• Node.js 18 + TypeScript
• 50K+ LOC across 200 files
• Jest test framework
• Express REST API with 30+ endpoints
• PostgreSQL database
• 15 developers contributing
• GitHub Actions CI/CD
• Current issues: high complexity in auth module, inconsistent test coverage, slow dependency updates

Task: Analyze this project and recommend:

1. Which fitness functions to implement (complexity, coverage, performance, dependencies)
2. Priority order (which will catch the most issues fastest)
3. Baseline thresholds (what limits make sense for THIS codebase, not aspirational goals)
4. Implementation plan (which tools to use, how to integrate with CI)

Format: For each fitness function, provide:

• Metric: What to measure
• Threshold: Acceptable limit based on current state
• Priority: High/Medium/Low
• Rationale: Why this matters for this specific codebase
• Implementation: Which tool/library to use - e.g., ts-complex, autocannon, npm outdated
• CI Integration: How to run in GitHub Actions - be specific

Focus Areas: Pay special attention to:

• Hotspot files (high complexity + frequent changes = risk)
• Critical paths (auth, payment, data access)
• Legacy modules (likely candidates for complexity violations)
• Public APIs (need strong test coverage)
• Performance-critical endpoints (user-facing, data-heavy)

Output: Provide a prioritized list of 3-5 fitness functions with specific thresholds and implementation steps. Start with the fitness function that will catch the most issues with the least effort.

🤖 AI Prompt #2: Generate Fitness Function Tests

Role: You are a software engineer implementing fitness functions as automated tests that run in CI/CD pipelines.

Context: I have a Node.js 18 + TypeScript project using Jest for testing and GitHub Actions for CI/CD.

Target metrics:

• Cyclomatic complexity ≤10 per function
• Test coverage ≥80% (line + branch)
• Dependency age ≤90 days
• Performance p95 <200ms for /api/* endpoints

Task: Generate 4 executable fitness function test files:

1. tests/fitness-functions/complexity.test.ts

   • Use ts-complex library to analyze TypeScript files
   • Check cyclomatic complexity for all functions in src/
   • Fail if any function exceeds 10
   • Report violations with file:line:function name
   • Suggest refactoring strategies in error message

2. tests/fitness-functions/coverage.test.ts

   • Read coverage/coverage-summary.json (generated by Jest)
   • Check line, branch, function, statement coverage
   • Fail if any metric <80%
   • Compare against baseline/coverage-baseline.json
   • Fail if coverage dropped >2% from baseline

3. tests/fitness-functions/dependency-freshness.test.ts

   • Run "npm outdated --json" to find old packages
   • Check publish date of each dependency using "npm view @ time.modified"
   • Fail if any dependency >90 days old
   • Warn if dependency >60 days old
   • Categorize by severity: critical (security), major (breaking), minor (safe)

4. tests/fitness-functions/performance.test.ts

   • Start test server programmatically
   • Use autocannon to load test GET /api/users and POST /api/orders
   • Measure p95, p99 latency and throughput
   • Compare against baseline/perf-baseline.json
   • Fail if p95 >200ms or regressed >10% from baseline
   • Clean up server process after test

Requirements:

• All tests must be Jest .test.ts files that can run with "npm test"
• Tests must fail with actionable error messages (include file paths, actual vs expected values)
• Thresholds should be configurable via environment variables (MAX_COMPLEXITY, MIN_COVERAGE, MAX_DEP_AGE_DAYS)
• Include helper functions for calculations (don't repeat code)
• Add JSDoc comments explaining what each function does

Also generate:

• .github/workflows/fitness-functions.yml (runs on every PR, uploads artifacts)
• baseline/coverage-baseline.json (example structure with 85% coverage)
• baseline/perf-baseline.json (example structure with p95: 145ms)
• README-FITNESS-FUNCTIONS.md (explains how to run tests and update baselines)

Output: Complete, executable TypeScript code for all files. Initially configure CI with continue-on-error: true (warning mode) so we can monitor for 2 weeks before switching to blocking mode.

Human Review Checklist

After AI generates fitness function tests, review the code carefully before running it:

• File Structure
  • ✓ Four test files in fitness-functions directory
  • ✓ GitHub Actions workflow for CI integration
  • ✓ Baseline files for tracking historical metrics
  • ✓ Documentation explaining how to run and update tests
• Complexity Analysis
  • ✓ Uses dedicated tool like ts-complex (not manual AST parsing or regex)
  • ✓ Correctly counts all branching structures (conditionals, loops, case statements, logical operators, exception handlers)
  • ✓ Error messages pinpoint exact location and suggest specific refactoring patterns
  • ✓ Threshold configurable through environment variables
  • ✓ Test by running locally and verifying error messages are actionable
• Coverage Validation
  • ✓ Reads Jest's coverage report and validates all four metrics (lines, branches, functions, statements)
  • ✓ Compares current coverage against stored baseline to detect regressions
  • ✓ Error messages show which metric failed, by how much, and remediation steps
  • ✓ Thresholds configurable for starting with realistic values
  • ✓ Test by generating coverage report first, then running fitness function
• Dependency Freshness
  • ✓ Checks actual publish dates of dependencies (not just version numbers)
  • ✓ Categorizes outdated packages by severity (security vs minor version bumps)
  • ✓ Provides clear upgrade paths
  • ✓ Warns before failing to give teams time to plan upgrades
  • ✓ Integrates with npm audit to flag known security vulnerabilities
  • ✓ Test by running check and verifying age calculations are accurate
• Performance Testing
  • ✓ Starts application programmatically and cleanly shuts down
  • ✓ Runs realistic load tests against critical endpoints
  • ✓ Measures both absolute latency (p95, p99) and regression from baseline
  • ✓ Properly cleans up spawned processes (no background servers left running)
  • ✓ Error messages show actual vs expected latency with percentage regression
  • ✓ Test to ensure clean startup, load test, and termination
• CI/CD Integration
  • ✓ Workflow runs on every pull request and push to main
  • ✓ Uses npm ci for deterministic dependency installation
  • ✓ Initially configured with continue-on-error: true for monitoring period
  • ✓ Uploads test results as artifacts for historical tracking
  • ✓ Optionally comments on pull requests with pass/fail summaries
  • ✓ After monitoring: change to blocking mode with continue-on-error: false
• Baseline Management
  • ✓ Baseline files contain realistic starting values based on current codebase
  • ✓ Files committed to Git with metadata (timestamp, commit SHA)
  • ✓ Documentation explains how to regenerate baselines
  • ✓ Update process: validate results, copy fresh metrics, commit with clear message
• Security Review
  • ✓ No hardcoded secrets
  • ✓ No arbitrary code execution patterns (eval, unsanitized exec calls)
  • ✓ No external network calls that could leak data
  • ✓ File system operations limited to project directory
  • ✓ No dependencies from unknown sources
  • ✓ Tests are self-contained and offline-first
  • ✓ Red flags: data exfiltration, arbitrary input execution, sensitive system access
• Final Validation
  • ✓ Install new dependencies required by tests
  • ✓ Run each fitness function individually to verify clear output
  • ✓ Run all tests together to check for conflicts or race conditions
  • ✓ Validate GitHub Actions workflow syntax before committing
  • ✓ Optional: test full CI pipeline locally using tools like act
  • ✓ Expected outcome: tests pass cleanly or fail with actionable error messages

Threat Modeling: Denial of Service (DoS)

Missing rate limiting is a classic DoS vulnerability. An attacker can exploit this by flooding your server with requests, making it unavailable to legitimate users. To prevent DoS attacks, it's essential to conduct threat modeling. Here we will focus on STRIDE threat modeling focusing on Denial of Service (D) threats.

🎯 What is Denial of Service?

• Definition: An attacker exhausts system resources or exploits design flaws to make services unavailable. Denial of Service threatens availability — the assurance that systems and data are accessible when needed.

Common Denial of Service Attack Vectors

• Resource Exhaustion: Consuming CPU, memory, disk, or network bandwidth
• Algorithmic Complexity Attacks: Exploiting O(n²) algorithms with carefully crafted inputs
• Regular Expression DoS (ReDoS): Catastrophic backtracking in regex patterns
• Missing Rate Limits: Unlimited API requests overwhelming servers
• Amplification Attacks: Small requests triggering large responses

Why It Matters

• DoS attacks cause business disruption, revenue loss, and reputation damage. E-commerce sites lose sales during downtime. SaaS platforms violate SLAs. Critical infrastructure (healthcare, finance) can endanger lives. Even brief outages erode customer trust and invite competitors.

Maps to OWASP

• Primary: A04 - Insecure Design (missing rate limits, algorithmic complexity)
• Secondary: A05 - Security Misconfiguration (resource limits, timeouts)

🤖 AI Prompt: Identify Denial of Service Threats in Architecture

Role: You are a security architect specializing in system reliability, performance engineering, and DoS prevention. Your task is to perform STRIDE threat modeling focusing on Denial of Service (D) threats.

Context: I have the following architecture:

PASTE YOUR ARCHITECTURE DIAGRAM OR DESCRIPTION HERE

Example:

• React SPA making API calls
• Node.js REST API with no rate limiting
• PostgreSQL database with expensive queries
• Search endpoint accepting user-provided regex patterns
• File upload endpoint (no size limit)
• Email sending endpoint (no throttling)
• WebSocket connections (unlimited per user)
• CPU-intensive operations (image processing, PDF generation)

Task: Analyze this architecture for Denial of Service threats. For each endpoint, resource, and operation, identify where an attacker could exhaust resources or degrade availability.

Format: For each threat, provide:

• Threat: One-line description
• Component: Which part of the system is vulnerable
• Attack Scenario: Step-by-step attack walkthrough
• Impact: What happens if successful — service outage, degraded performance, etc.
• Likelihood: High/Medium/Low based on attacker effort vs impact
• Mitigation: Specific controls to prevent or detect this attack
• OWASP Mapping: A04, A05, etc.
• Code Example: Show vulnerable pattern and DoS-resistant fix

Focus Areas: Pay special attention to:

• API endpoints without rate limiting
• Database queries with no pagination or timeouts
• User-controlled input affecting algorithmic complexity (sort, search, regex)
• File uploads and processing
• Computationally expensive operations (crypto, image processing, PDF generation)
• WebSocket or long-polling connections
• Email/SMS sending endpoints
• Memory-intensive operations (large object creation, caching)
• Third-party API calls (could be slow or unreliable)

Output: Provide 3-5 high-priority denial of service threats with complete details. Prioritize threats that require minimal attacker resources for maximum impact.

Conclusion

Missing rate limiting is a serious security vulnerability that can lead to DoS attacks, brute-force attacks, and resource exhaustion. By implementing rate limiting middleware, you can protect your application from these threats. Remember to choose a rate limiting strategy that's appropriate for your application's needs, and be sure to test your implementation thoroughly. Stay secure, guys!