LLM-Powered Bug Bounties: When AI Doubles the Attack Surface + Video

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Introduction:

The integration of Large Language Models into application development has fundamentally altered the security landscape—not by introducing entirely new classes of vulnerabilities, but by exponentially amplifying existing ones. As Tom MacWright, founder of Val Town, observed during his bug-hunting and bug-bounty management experience, “Security in the age of LLMs is like security before LLMs, there’s just more of it.” This means traditional vulnerabilities—broken access control, insecure API endpoints, and privilege escalation—now appear at greater scale and velocity, accelerated by AI-assisted code generation that can produce exploitable patterns at machine speed.

Learning Objectives:

  • Understand how LLM integration expands the attack surface and accelerates vulnerability discovery
  • Master the configuration of API authentication middleware (OAuth 2.0) for securing AI-powered applications
  • Implement responsible disclosure practices and bug bounty program management for LLM-enabled platforms
  • Learn practical command-line and code-level techniques for hardening cloud-based development environments

You Should Know:

  1. The API Ownership Bypass: A Case Study in LLM-Accelerated Vulnerabilities

In June 2023, Val Town introduced a new system for renaming and saving vals (serverless TypeScript functions). The implementation inadvertently failed to check all ownership conditions before allowing new versions of vals to be saved. This meant that any registered user who possessed a valid API token and another user’s val ID could save and run new versions of that user’s vals—a classic Insecure Direct Object Reference (IDOR) vulnerability.

The vulnerability persisted for over a year before being discovered during routine product development. A fix was deployed within hours on July 22, 2024. In the LLM era, such flaws are particularly dangerous because AI agents can autonomously scan for and exploit them at scale. LLMs excel at pattern matching—feeding them a description of a prior vulnerability pattern is an effective way to identify potential new ones.

Step-by-Step Guide: Securing API Endpoints Against IDOR

1. Implement ownership validation at the controller level:

// Example: Val Town's API ownership check (conceptual)
async function saveVal(valId: string, userId: string, content: any) {
const val = await db.getVal(valId);
if (val.ownerId !== userId) {
throw new Error('Unauthorized: You do not own this val');
}
// Proceed with save operation
return await db.updateVal(valId, content);
}

2. Use middleware for consistent authorization:

// Express-style middleware pattern
function requireOwnership(req, res, next) {
const val = await Val.findById(req.params.valId);
if (val.userId !== req.user.id) {
return res.status(403).json({ error: 'Forbidden' });
}
next();
}
  1. Audit all internal API endpoints—not just public ones. The Val Town vulnerability affected internal APIs, not the publicly documented OpenAPI endpoints.

  2. Implement comprehensive test suites that specifically target authorization logic:

    Run authorization test suite
npm run test:auth -- --coverage

5. Log and monitor all unauthorized access attempts:

 Linux: Monitor API access logs for 403 errors
tail -f /var/log/nginx/access.log | grep " 403 "

2. Authentication Infrastructure: From Clerk to Better Auth

Val Town’s authentication journey reflects the challenges of securing modern cloud platforms. The platform transitioned from Clerk to Better Auth after encountering significant reliability issues—Clerk’s rate limits and single point of failure for sessions caused instability for their social platform.

This migration highlights a critical lesson: authentication is not a “set and forget” component. In LLM-powered environments, where automated agents may generate thousands of requests per second, rate limiting and session management become existential concerns.

Step-by-Step Guide: Implementing OAuth 2.0 with Val Town’s Standard Library

Val Town provides a `std/oauth` middleware that handles the full OAuth 2.0 flow, stores sessions in encrypted cookies (no database needed), and provides access to the user’s Val Town profile.

1. Install and import the OAuth middleware:

import { oauth } from "https://esm.town/std/oauth";

2. Wrap any val with the authentication layer:

export const myVal = oauth(async (req: Request) => {
// This code only runs for authenticated users
const user = req.user; // Val Town user profile
return new Response(<code>Hello, ${user.name}!</code>);
});
  1. Configure allowed redirect URIs in your Val Town dashboard.

4. Test the authentication flow:

 Curl command to test protected endpoint
curl -X GET https://api.val.town/v1/protected \
-H "Authorization: Bearer YOUR_ACCESS_TOKEN"

5. For Windows environments, use PowerShell:

Invoke-RestMethod -Uri "https://api.val.town/v1/protected" `
-Headers @{Authorization = "Bearer YOUR_ACCESS_TOKEN"}

3. Responsible Disclosure and Bug Bounty Program Management

Managing a bug bounty program in the LLM era requires a carefully calibrated approach. Val Town’s program exemplifies best practices: they offer bounties commensurate with severity, maintain clear in-scope/out-of-scope definitions, and commit to confidentiality and non-prosecution for ethical researchers.

In-scope domains for Val Town’s program include:

– `val.town`
– `valtown.email`
– `api.val.town`
– `esm.town`
– `.val.run`

Out-of-scope issues include:

  • Vulnerabilities on outdated browsers or open source libraries
  • Missing security hardening headers
  • Automated tool reports
  • SPF/DMARC/DKIM/CAA/BIMI policy issues
  • Self-XSS or developer console code execution
  • Login/logout CSRF
  • Bugs on Vals themselves (user-controlled code)

Step-by-Step Guide: Setting Up a Bug Bounty Program

  1. Define scope clearly—specify which domains, subdomains, and services are eligible.

2. Establish a dedicated security contact (e.g., `[email protected]`).

3. Create a responsible disclosure policy:

  • Prohibit data exfiltration beyond what’s necessary to demonstrate the vulnerability
  • Prohibit modifying or deleting other users’ data
  • Require confidentiality until the issue is resolved
  • Exclude physical security, social engineering, DDoS, and spam attacks
  1. Offer graduated bounties based on severity (Critical, High, Medium, Low).

  2. Commit to non-prosecution for researchers acting in good faith.

  3. Publish security advisories post-fix, as Val Town did with their Security Disclosure.

4. Cloud Hardening for LLM-Enabled Development Environments

Val Town provides a cloud-based coding environment where users write TypeScript functions (vals) that run instantly—HTTP handlers, cron jobs, email handlers, and SQLite, all from a browser editor. This architecture introduces unique security challenges: user-controlled code executes in a shared environment, and LLM-powered code generation can inadvertently introduce vulnerabilities.

Step-by-Step Guide: Hardening Cloud Development Platforms

  1. Implement strict input validation for all user-supplied parameters: 
    // Validate val name
function validateValName(name: string): boolean {
return /^[a-zA-Z0-9_-]{1,50}$/.test(name);
}

  2. Sanitize outputs to prevent XSS and injection attacks:

    function sanitizeOutput(data: any): string {
return JSON.stringify(data).replace(/[<>]/g, '');
}

  3. Enforce resource limits (CPU, memory, execution time) to prevent DoS:

    Linux: Set ulimit for process
ulimit -t 30  30 seconds CPU time
ulimit -v 512000  512MB virtual memory

  4. Isolate user code execution using containers or sandboxing:

    Docker run with restricted capabilities
docker run --rm --cap-drop=ALL --cap-add=NET_BIND_SERVICE \
--read-only --tmpfs /tmp:rw,noexec,nosuid,size=64m \
user-code:latest

5. Implement comprehensive logging:

 Linux: Log all val executions
journalctl -u val-town.service -f --since "1 hour ago"

6. For Windows Server:

 Set process affinity and priority
Get-Process -1ame "val-runner" | ForEach-Object {
$<em>.ProcessorAffinity = 0x0003  Use only first 2 cores
$</em>.PriorityClass = "BelowNormal"
}
  1. LLM-Specific Security: Treating AI Agents as Potentially Malicious Actors

The OWASP LLM Top 10 highlights critical vulnerabilities including prompt injection, insecure output handling, and excessive agency. Security experts now recommend treating LLMs and AI agents as potentially malicious actors, implementing strict input validation, output sanitization, and access control.

Step-by-Step Guide: Mitigating LLM-Specific Risks

1. Validate all LLM inputs against allowlists:

function validatePrompt(prompt: string): boolean {
// Block known injection patterns
const blocked = ['ignore previous', 'system prompt', 'you are now'];
return !blocked.some(pattern => prompt.toLowerCase().includes(pattern));
}

2. Sanitize LLM outputs before execution or display:

function sanitizeCodeOutput(output: string): string {
// Remove potentially dangerous JavaScript
return output.replace(/eval(/g, '/ blocked /');
}

3. Implement output filtering for generated code:

 Linux: Scan generated code for dangerous patterns
grep -E "(eval|exec|require|import|child_process)" generated_code.ts
  1. Use content security policies to restrict execution contexts: 
    <meta http-equiv="Content-Security-Policy" 
content="script-src 'self' https://api.val.town">

5. Monitor for anomalous patterns in LLM usage:

 Log analysis for suspicious prompts
grep -E "(password|token|secret|key)" /var/log/llm/prompts.log
  1. Vulnerability Exploitation and Mitigation: The LLM Acceleration Factor

The window between CVE disclosure and exploit deployment is shrinking rapidly. With LLMs capable of autonomously hacking websites and exploiting one-day vulnerabilities, organizations must adopt proactive security postures.

Step-by-Step Guide: Rapid Vulnerability Response

1. Establish a patch management pipeline:

 Automated vulnerability scanning
npm audit --json > vulnerabilities.json
  1. Implement Web Application Firewall (WAF) rules for known patterns: 
    Nginx WAF rule for IDOR attempts
location /api/vals/ {
if ($arg_userId != $cookie_userId) {
return 403;
}
}

  2. Use static analysis tools to detect vulnerabilities before deployment:

    Run ESLint with security plugins
npx eslint --plugin security .

4. Conduct regular penetration testing, including AI-assisted testing:

 Run OWASP ZAP automated scan
zap-cli quick-scan --spider -r https://api.val.town

5. Maintain an incident response plan:

 Create encrypted backup before emergency patches
tar -czf backup-$(date +%Y%m%d).tar.gz /var/www
gpg -c backup-.tar.gz

What Undercode Say:

  • LLMs don’t create new vulnerabilities—they accelerate the discovery and exploitation of existing ones. The fundamental security principles (least privilege, defense in depth, secure defaults) remain unchanged, but their implementation must now account for AI-powered adversaries that operate at machine speed.

  • Bug bounty programs must evolve to address LLM-specific risks. This includes defining clear scope boundaries for AI-generated code, establishing protocols for handling AI-discovered vulnerabilities, and potentially offering enhanced bounties for vulnerabilities that could be autonomously exploited by LLM agents.

The Val Town experience demonstrates that the most dangerous vulnerabilities are often the simplest: an ownership check that wasn’t properly implemented, an internal API that wasn’t adequately secured. In the LLM era, these “simple” flaws become critical because they can be discovered and exploited by automated systems at scale. Organizations must prioritize foundational security hygiene—comprehensive testing, rigorous access controls, and rapid incident response—while simultaneously addressing the unique challenges posed by AI integration. The security community must also reckon with the reality that LLMs can hallucinate vulnerability reports, creating noise that obscures genuine threats and wastes valuable researcher time.

Prediction:

  • +1 Bug bounty programs will increasingly incorporate LLM-powered automated testing, with AI agents competing alongside human researchers to discover vulnerabilities. This will drive down the cost of security testing but increase the volume of reports requiring triage.

  • -1 The acceleration of vulnerability discovery by LLMs will outpace the industry’s ability to patch, leading to a “zero-day gap” where the average time-to-exploit drops below the average time-to-patch for critical vulnerabilities.

  • +1 Organizations that adopt proactive security measures—comprehensive test suites, automated scanning, and rapid deployment pipelines—will gain a competitive advantage by reducing their mean time to remediation (MTTR).

  • -1 The democratization of hacking through LLM-powered tools will lower the barrier to entry for malicious actors, increasing the overall volume of attacks and making security a more significant operational burden for all organizations.

  • +1 Standards like the OWASP LLM Top 10 will mature, providing clear guidance for securing AI-powered applications and reducing the prevalence of common vulnerabilities.

  • -1 The hallucination problem in LLM security tools will persist, generating false positives that overwhelm security teams and potentially masking genuine vulnerabilities.

  • +1 Cloud platforms like Val Town will lead the way in developing secure-by-default AI development environments, setting industry standards for LLM security that others will follow.

  • -1 The complexity of securing LLM-powered systems will increase the demand for specialized security expertise, creating a talent shortage that leaves many organizations under-protected.

▶️ Related Video (90% Match):

https://www.youtube.com/watch?v=50e3h0Q7RTE

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