Trusting AI Output Without Validation? It’s the New XSS Risk

Read full article from Auth0 here:  https://auth0.com/blog/owasp-llm05-improper-output-handling/?utm_source=nhimg

 

As AI adoption skyrockets, developers are increasingly treating Large Language Model (LLM) outputs as trusted data. This creates a new class of vulnerabilities—Improper Output Handling (LLM05)—where malicious actors exploit LLMs to inject unsafe content into applications. The consequences? Classic attacks like XSS, SQL Injection, and Remote Code Execution (RCE) return, now amplified through AI.

Key Takeaways:

• The Core Problem: LLMs are treated as trusted components, but their outputs are unpredictable. Prompt Injection (LLM01) can trick models into generating malicious payloads.

• XSS Risks: Displaying AI output with .innerHTML or similar methods executes embedded scripts. Use .textContent or sanitized rendering to neutralize attacks.

• SQL Injection Risks: Never execute raw SQL generated by AI. Instead, parse output as structured data and safely parameterize queries.

• RCE & Command Injection Risks: Avoid executing AI-generated shell commands. Expose a controlled set of functions (“tools”) with sandboxing and strict validation.

• Prevention Principles:

  1. Zero-trust mindset: Treat all AI output as untrusted.

  2. Context-aware encoding: Sanitize and encode for the specific execution context (HTML, SQL, system commands).

  3. Principle of Least Privilege: Limit permissions for AI actions and enforce authorization checks.

  4. Defense-in-depth: Implement multiple layers—CSP, schema validation, logging, and monitoring.

Bottom Line:
Improper output handling is the new XSS. AI outputs must be treated like user input—never trusted by default. By applying zero-trust principles, context-aware encoding, and strict authorization, developers can safely harness AI capabilities while preventing severe security incidents.