New Research and PoC Reveal Security Risks in LLM-Based Coding

A recent investigation has uncovered that relying solely on large language models (LLMs) to generate application code can introduce critical security vulnerabilities, according to a detailed blog post published on August 22, 2025.

The research underscores that LLMs, which are trained on broad internet data—much of it insecure example code—often replicate unsafe patterns without warning developers to potential risks.

Security experts have long cautioned that code examples found online prioritize demonstration over defense.

However, LLMs amplify this problem by learning and regurgitating those insecure snippets at scale. In one notable case, the researcher discovered a vulnerability in sample code for a pay-per-view plugin from a leading cryptocurrency platform.

Although the flaw existed only in the example implementation and not the core library, it could still be copied into production applications, where it might go unnoticed by security reviews.

The blog post’s centerpiece is a live proof-of-concept (PoC) demonstrating how client-side code generated by an LLM exposed an email-sending API endpoint directly in browser JavaScript.

The vulnerable script defined the API URL, input validation, and submission logic entirely on the front end:

const smtp_api = "https:///send-email";
function validateForm(name, email, number) {
  if (!name || !email || !number) {
    alert("Please fill in all required fields.");
    return false;
  }
  return true;
}

async function submitForm(name, email, number) {
  const data = { name, email, number, company_email: "", project_name: "" };

  const res = await fetch(smtp_api, {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify(data)
  });
  // …
}

By exposing these details on the client side, any malicious actor can craft requests outside the intended workflow, bypassing validation altogether.

The PoC employs a simple cURL command to simulate attack scenarios:

curl -X POST "https://redacted.example/send-email" 

  -H "Content-Type: application/json" 

  -d '{"name":"Test User","email":"[email protected]","number":"1234567890","country_code":"+91","company_email":"[email protected]","project_name":"Redacted Project"}'

This trivial exploit demonstrates the ability to spam arbitrary email addresses, target customers with phishing messages, or impersonate trusted senders—threats that escalate quickly when sample code is auto-generated.

Upon reporting the flaw to the hosting provider, the researcher was told remediation was out of scope, as the vulnerable app was a third-party example.

Nonetheless, the episode highlights a broader issue: LLMs lack understanding of business context and threat modeling, and cannot reason about abuse cases or defense design on their own. Human oversight remains essential to identify attack surfaces and enforce secure defaults.

As organizations increasingly integrate AI into development workflows, this research serves as a stark reminder that security cannot be an afterthought.

Combining LLM assistance with rigorous human-led code reviews, threat modeling, and automated security testing will be crucial to prevent LLM-generated vulnerabilities from reaching production environments.

Find this News Interesting! Follow us on Google News, LinkedIn, and X to Get Instant Updates!