Security Vulnerability: Missing Rate Limiting In App.ts

Oct 15, 2025 by ADMIN 56 views

Hey guys! Today, we're diving deep into a critical security vulnerability that can leave your applications wide open to attacks. We're talking about the dreaded js/missing-rate-limiting issue, specifically found in app.ts at line 176 in the AliceNN-ucdenver/agent-test repository. This is a high-severity problem, so let's buckle up and get ready to understand what's going on and how to fix it!

Understanding the Vulnerability

So, what exactly does js/missing-rate-limiting mean? Simply put, it means that a part of your application, usually a route handler or API endpoint, isn't protected against abuse through rate limiting. Rate limiting is a crucial security mechanism that restricts the number of requests a user or IP address can make within a specific timeframe. Without it, your application can be vulnerable to a whole bunch of nasty attacks.

Think of it this way: imagine a login form without any restrictions. An attacker could write a script to try thousands of username and password combinations every minute, attempting to brute-force their way into user accounts. Or picture an API endpoint that fetches sensitive data; without rate limiting, an attacker could flood the endpoint with requests, potentially overwhelming your server and causing a denial-of-service (DoS).

Missing rate limiting falls under the OWASP A04 category: Insecure Design. This means the vulnerability stems from a flaw in the application's architecture, not just a simple coding mistake. Fixing it requires careful consideration of your application's design and security requirements.

In this specific case, the vulnerability was detected by CodeQL v2.23.2 on October 13, 2025, highlighting the importance of using automated tools to identify security issues. The vulnerable code resides in src/app.ts between lines 176 and 217, and it involves a database access that isn't rate-limited, making it a prime target for abuse.

Diving Deeper: OWASP Insecure Design

Let's zoom out for a moment and talk about OWASP A04: Insecure Design. This category is all about security flaws that are baked into the application's design before any code is even written. These aren't your run-of-the-mill implementation bugs; they're fundamental weaknesses in the architecture.

Insecure design can manifest in many ways, such as:

Predictable tokens: Using sequential IDs or timestamps for tokens, making them easily guessable.
Missing rate limiting: As we've already discussed, this allows for brute-force attacks and resource exhaustion.
Business logic flaws: Issues like allowing negative quantities in orders or transfers to oneself.
Insufficient threat modeling: Failing to consider security during the design phase.
No token expiration: Tokens that are valid forever, creating a huge security risk.
Reusable tokens: Tokens that can be used multiple times, such as password reset tokens.

Insecure design is a big deal because it's costly to fix after deployment. It often requires architectural changes rather than simple code patches. That's why it's so important to think about security from the very beginning of a project.

How Insecure Design Maps to STRIDE

The STRIDE threat model is a handy way to categorize security threats. Insecure design primarily maps to Spoofing, where predictable tokens can be used to impersonate users. It also has secondary connections to Information Disclosure, where design flaws leak system state, and Tampering, where business logic can be bypassed.

Understanding Attack Vectors and Risks

So, how can an attacker exploit a missing rate-limiting vulnerability? There are several attack vectors to consider:

Brute-force attacks: Repeatedly trying different passwords or API keys until the correct one is found.
Denial-of-service (DoS) attacks: Flooding the server with requests to overwhelm its resources and make it unavailable to legitimate users.
Resource exhaustion: Consuming excessive server resources like database connections or memory.
Account enumeration: Attempting to identify valid usernames or email addresses by repeatedly requesting password resets.

The risks associated with these attacks are significant:

Account takeover: Attackers gaining unauthorized access to user accounts.
Data breaches: Sensitive information being exposed due to compromised accounts or overwhelmed systems.
Service outages: Applications becoming unavailable, leading to business disruption and revenue loss.
Reputational damage: Loss of trust from users and customers.

Remediation: Implementing Secure Design Patterns

Alright, enough doom and gloom! Let's talk about how to fix this vulnerability. The key is to implement secure design patterns, focusing on:

Cryptographically Secure Token Generation: Use crypto.randomBytes() to generate unpredictable tokens. Don't derive tokens from user data or timestamps. Hash the tokens before storing them in the database.
Token Expiration: Set a maximum validity period for tokens (e.g., 30 minutes for password reset tokens). Store the creation timestamp with the token and validate its age on verification. Automatically clean up expired tokens.
One-Time Token Usage: Mark tokens as used after successful verification. Reject any attempts to reuse them. Store a usage flag in the token metadata.
Rate Limiting: Implement rate limiting on sensitive operations, such as login, password reset requests, and API calls. Use a library like express-rate-limit to make this easier. Provide generic error messages to avoid revealing rate limit details.
Defense in Depth: Use multiple layers of security controls. For example, rate limiting, token expiration, and one-time token usage can all work together to protect against attacks.

Example: Securing Password Reset Flow

Let's look at an example of how to secure a password reset flow:

Before (Vulnerable Code):

// ❌ CRITICAL: Predictable token generation
export function generateResetToken(email: string): string {
  // ❌ Token is predictable (email + timestamp)
  return email + Date.now();
}

// ❌ CRITICAL: No expiration, no rate limiting
const resetTokens = new Map<string, string>();

export function requestPasswordReset(email: string): string {
  // ❌ No rate limiting - unlimited requests
  // ❌ Token never expires
  // ❌ Token can be reused
  const token = generateResetToken(email);
  resetTokens.set(token, email);
  return token;
}

After (Secure Code):

// ✅ SECURE: Cryptographically random tokens with comprehensive security controls
import crypto from 'crypto';
import bcrypt from 'bcrypt';

interface ResetToken {
  tokenHash: string;
  email: string;
  createdAt: Date;
  used: boolean;
}

// In-memory store (use Redis/database in production)
const resetTokens = new Map<string, ResetToken>();
const rateLimits = new Map<string, number[]>();

const TOKEN_EXPIRY_MS = 30 * 60 * 1000; // 30 minutes
const MAX_REQUESTS_PER_HOUR = 3;
const RATE_LIMIT_WINDOW_MS = 60 * 60 * 1000; // 1 hour

// ✅ Rate limiting with defense in depth
function checkRateLimit(identifier: string, maxRequests: number, windowMs: number): boolean {
  const now = Date.now();
  const windowStart = now - windowMs;

  // Get recent requests for this identifier
  const requests = rateLimits.get(identifier) || [];
  const recentRequests = requests.filter(time => time > windowStart);

  if (recentRequests.length >= maxRequests) {
    console.warn('Rate limit exceeded', {
      identifier: identifier.substring(0, 3) + '***',
      attempts: recentRequests.length,
      timestamp: new Date().toISOString()
    });
    return false; // Rate limit exceeded
  }

  // Record this request
  recentRequests.push(now);
  rateLimits.set(identifier, recentRequests);
  return true;
}

// ✅ Cryptographically secure token generation
export async function generateResetToken(email: string): Promise<string> {
  // ✅ Check rate limit (defense in depth layer 1)
  if (!checkRateLimit(email, MAX_REQUESTS_PER_HOUR, RATE_LIMIT_WINDOW_MS)) {
    // ✅ Generic error message (don't reveal rate limit details)
    throw new Error('Please try again later');
  }

  // ✅ Generate cryptographically secure random token (256-bit)
  const token = crypto.randomBytes(32).toString('hex');

  // ✅ Hash token before storing (defense in depth layer 2)
  const tokenHash = await bcrypt.hash(token, 10);

  // ✅ Store token metadata with expiration and usage tracking
  resetTokens.set(tokenHash, {
    tokenHash,
    email,
    createdAt: new Date(), // Track creation time (defense in depth layer 3)
    used: false // One-time use flag (defense in depth layer 4)
  });

  // ✅ Log security event (no sensitive data)
  console.log('Password reset requested', {
    email: email.substring(0, 3) + '***',
    timestamp: new Date().toISOString()
  });

  // ✅ Return plaintext token (sent via email, only once)
  return token;
}

The secure code example uses:

crypto.randomBytes() to generate cryptographically secure tokens.
bcrypt to hash the tokens before storing them.
Rate limiting to prevent abuse.
Token expiration to limit the window of opportunity for attackers.
One-time token usage to prevent replay attacks.
Defense in depth by combining multiple security controls.

Human Review Checklist

Before merging any AI-generated secure design code, it's crucial to perform a thorough human review. Here's a checklist to guide you:

Token Unpredictability:
- Are tokens generated using crypto.randomBytes with sufficient entropy?
- Are tokens never derived from predictable sources?
- Is the operating system CSPRNG used via the crypto module?
- Are tokens base64 or hex encoded for safe transmission?
- Are there no discernible patterns in token generation?
Token Expiration:
- Does every token have an explicit expiration time?
- Are password reset tokens expiring within a reasonable timeframe?
- Is expiration validated on every token use?
- Are expired tokens immediately deleted from storage?
- Are attempts to use expired tokens logged?
One-Time Token Usage:
- Are tokens for sensitive operations single-use only?
- Is the token marked as used after successful verification?
- Are subsequent attempts to use consumed tokens rejected?
- Are token reuse attempts logged?
Rate Limiting:
- Is rate limiting implemented on all security-sensitive operations?
- Are the rate limits appropriate for the use case?
- Is the implementation server-side?
- Are generic error messages used when rate limits are exceeded?
Token Storage Security:
- Are tokens never stored in plaintext?
- Is token verification using hash comparison with constant-time comparison?
- Is token metadata stored separately?
Defense in Depth:
- Are multiple independent security layers implemented?
- Does each layer fail independently without compromising others?
Business Logic Validation:
- Do all business operations validate inputs against business rules?
- Are negative quantities, self-transfers, etc., prevented?
Security Event Logging:
- Are all security-relevant events logged?
- Do logs include sufficient context for investigation?
- Are token values, passwords, and secrets never logged?

Next Steps

Analyze your codebase: Use tools like CodeQL or AI prompts to identify insecure design vulnerabilities.
Prioritize findings: Focus on critical and high-severity issues first.
Implement secure design patterns: Use the techniques we've discussed, such as cryptographically secure tokens, rate limiting, and defense in depth.
Review generated code: Use the human review checklist to ensure quality and security.
Test thoroughly: Verify token randomness, expiration, one-time use, and rate limiting.
Conduct threat modeling: Identify security requirements before implementation.
Apply defense in depth: Use multiple independent security layers.
Implement business logic validation: Validate against business rules.
Monitor and iterate: Log security events, analyze patterns, and improve your design.

Fitness Functions: Maintaining Architectural Integrity

To prevent future regressions, consider implementing fitness functions. These are automated, objective quality gates that continuously validate architectural characteristics.

Fitness functions can measure metrics like:

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).

By failing builds when code degrades beyond acceptable thresholds, fitness functions help maintain a secure and maintainable architecture over time.

Threat Model Analysis: Preventing Denial of Service

Let's shift gears and discuss Denial of Service (DoS), a major threat that can be mitigated by proper rate limiting and secure design.

DoS attacks aim to make a system unavailable to legitimate users. Common attack vectors include:

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.

STRIDE: Denial of Service

When threat modeling for DoS, we focus on the Denial of Service (D) category in the STRIDE model. This means we're looking at threats to the availability of our system.

To identify DoS threats, consider:

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).

Example DoS Threat: ReDoS in Search Endpoint

Let's look at a specific example: a Regular Expression DoS (ReDoS) attack in a search endpoint.

Threat: Attacker supplies malicious regex causing catastrophic backtracking and CPU exhaustion.

Component: /api/search?q=<regex> endpoint.

Attack Scenario:

Search endpoint accepts user input as regex: /api/search?q=^(a+)+$.2. Attacker sends pathological regex: ^(a+)+$ with input aaaaaaaaaaaaaaaaaaaaX.3. JavaScript regex engine enters catastrophic backtracking (O(2^n) time).4. Single request consumes 100% CPU for 30+ seconds.5. Attacker sends 10 concurrent requests, pinning all CPU cores.6. All legitimate requests timeout, service becomes unavailable.

Impact: Complete service outage with minimal attacker resources.

Likelihood: High (ReDoS is easy to exploit if regex is user-controlled).

Mitigation:

Never allow user-controlled regex patterns.
Use safe search alternatives (LIKE with wildcards, full-text search).
Implement per-user rate limiting.
Set query timeouts at the database level.
Monitor CPU usage and alert on sustained >80% utilization.
Use ReDoS detection libraries in CI/CD.

Claude Remediation Zone

If you're using Claude, you can ask it to provide a remediation plan for a vulnerability by commenting:

@claude Please provide a remediation plan for this vulnerability following the security and maintainability guidelines above.

Remember to review the generated plan carefully and ensure it addresses the root cause of the vulnerability without introducing new issues.

Final Thoughts

So, there you have it! We've taken a deep dive into the js/missing-rate-limiting vulnerability, explored OWASP Insecure Design, discussed secure design patterns, and touched on fitness functions and DoS threat modeling.

Remember, security is a continuous process. It's not something you can just bolt on at the end. By thinking about security from the beginning, implementing secure design patterns, and using automated tools to identify vulnerabilities, you can build more resilient and trustworthy applications.

Stay safe out there, guys, and keep those apps secure!