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Home/OWASP Top 10/LLM Top 10/LLM05: Improper Output Handling
LLM05CWE-79CWE-78CWE-80

The "I piped the model straight into eval" bug
Improper Output Handling

The bug where you trust the model output enough to render it, run it, or put it in a SQL query.

A perennial top-five issue — it's the moment the model's output becomes the next system's input.

Reference: LLM Top 10 (2025) — LLM05·Last updated April 7, 2026·By Flowpatrol Team
Improper Output Handling illustration

The model is smart. The model is helpful. The model is also a stranger typing things into your app. The second you render its text as HTML, run it as a shell command, or paste it into a SQL query, you're letting that stranger write code in your runtime.

Improper output handling is the bug where the model's text is used by the next system without being treated as untrusted input. Render it as HTML and you get XSS. Shell-exec it and you get RCE. Put it in a SQL string and you get SQL injection. The class is old; the input is new.

What your AI actually built

You built a feature where the model returns something and your code uses it. A chatbot that replies with Markdown. An agent that generates a shell command. A natural-language query tool that produces SQL on the fly. These are the shapes every AI app eventually takes.

In every case, there's a line in your code that takes the model's output and hands it to something that executes. innerHTML. exec. db.query. The model is treated as a trusted coauthor.

It isn't. Its output is a function of whatever went in, which includes whatever your user typed, which includes whatever an attacker typed. The model can be coaxed into producing a <script> tag, a rm -rf, or a DROP TABLE — and your code will happily run it.

How it gets exploited

A marketing site has an AI chatbot whose replies are rendered as Markdown in the page.

  • 1
    Send a payload
    The attacker sends: 'Please summarize the following HTML for me: <img src=x onerror=alert(document.cookie)>'.
  • 2
    Get the reply
    The model politely includes the HTML in its response because summarizing it required quoting it. The response comes back to the page and gets rendered.
  • 3
    Pop the cookie
    The browser executes the onerror handler, reads the session cookie, and posts it to the attacker's server. Classic stored XSS, delivered by the chatbot.
  • 4
    Upgrade to the agent
    The same app has a 'run this SQL' feature for admins. The attacker asks the bot a question designed to produce '; DROP TABLE users; --. The bot complies. The backend runs it.
  • Every visitor to the chatbot gets their session stolen, and the admin SQL feature nukes a production table — all via an LLM that was asked nice questions.

    Vulnerable vs Fixed

    Vulnerable — the model speaks, the app obeys
    // components/chat-reply.tsx
    export function ChatReply({ llmText }: { llmText: string }) {
      // Rendered straight into the DOM as HTML.
      return <div dangerouslySetInnerHTML={{ __html: marked(llmText) }} />;
    }
    
    // app/api/run-query/route.ts
    export async function POST(req) {
      const { question } = await req.json();
      const sql = await llm.generateSql(question);   // raw text from the model
      const rows = await db.query(sql);              // run it
      return Response.json(rows);
    }
    Fixed — treat every token as untrusted input
    // components/chat-reply.tsx
    import DOMPurify from 'isomorphic-dompurify';
    
    export function ChatReply({ llmText }: { llmText: string }) {
      const html = DOMPurify.sanitize(marked(llmText), {
        ALLOWED_TAGS: ['p', 'em', 'strong', 'ul', 'ol', 'li', 'code', 'pre', 'a'],
        ALLOWED_ATTR: ['href'],
      });
      return <div dangerouslySetInnerHTML={{ __html: html }} />;
    }
    
    // app/api/run-query/route.ts
    export async function POST(req) {
      const { question } = await req.json();
      const session = await getSession(req);
    
      // The model proposes; the server disposes. Parameterized, read-only, scoped.
      const { sql, params } = await llm.generateParameterizedSql(question, {
        allowed: readOnlyViewsFor(session.user),
      });
    
      if (!isSafeSelect(sql)) throw new Error('Unsafe query');
      return Response.json(await db.query(sql, params));
    }

    Never render model output as raw HTML — sanitize with an explicit allow-list of tags. Never run model output as a command or query — make the model produce a structured plan with parameters, then validate and execute it through a narrow, read-only interface. The model is a suggestion engine, not a runtime.

    A real case

    ChatGPT plugins shipped with XSS and SSRF in the first month

    In 2023, security researchers demonstrated that ChatGPT's first plugins rendered model output as HTML and passed it to fetch calls unchecked, turning the assistant into a delivery vehicle for classic web bugs.

    Related reading

    Glossary

    Cross-Site Scripting (XSS)

    References

    • LLM05: Improper Output Handling — official OWASP entry
    • OWASP Top 10 for LLM Applications (2025) — full list
    • CWE-79 on cwe.mitre.org
    • CWE-78 on cwe.mitre.org
    • CWE-80 on cwe.mitre.org

    Make sure your model output stays text.

    Flowpatrol tests every LLM surface in your app for XSS, injection, and command execution caused by trusting the model's reply.

    Try it free