No. Treat model output as untrusted input, the same as anything a user typed. OWASP calls the failure improper output handling: insufficient validation, sanitization and handling of generated output before it reaches other components. The fix uses the controls you already know, applied at the boundary.
What the risk is
OWASP's 2025 list for LLM applications includes Improper Output Handling as LLM05. The page explains that, because prompt input can steer the output, passing that output straight to a backend gives indirect access to extra functionality. Consequences it names include XSS, CSRF, SSRF, privilege escalation and remote code execution.
It separates this from overreliance. One concerns trusting the accuracy of an answer. This one concerns what happens when the answer is handed to a system without checks.
This applies to code you ship too. If an application asks a model to generate a query, a command or markup at runtime, that is output handling. Code written by an assistant that does this should be reviewed for the same reason.
Controls by destination
| Where the output goes | Control OWASP recommends |
|---|---|
| Database | Parameterized queries or prepared statements for all operations involving model output |
| Web page | Context-aware encoding, such as HTML encoding, plus a strict Content Security Policy |
| Any backend function | Validate responses first, following OWASP ASVS guidance |
| Everything | Treat the model like any other user, with a zero-trust stance |
| Operations | Log and monitor for unusual output patterns |
Steps to check an existing feature
- List every place model output is used by code, not by a person.
- For each, name the destination: SQL, shell, HTML, file path, API call.
- Check that the destination uses its safe interface, such as a prepared statement.
- Put a schema check in front of structured output and reject anything that does not match.
- Run with the least privilege the destination needs, so a bad output has limited reach.
- Add a test with hostile-looking output and confirm it is neutralised.
A common trap
Teams add a prompt that says to return only safe SQL. That is a request, not a control. Enforcement belongs in the code that receives the output. See also prompt injection risks in AI code review bots for a concrete case.
Where Safeguard fits
Safeguard runs SAST and dynamic scans that flag injection patterns and unsafe sinks in application code, and policies can gate a merge on them. A free scan is a quick start.
Try it yourself
Click through it at your own pace, or open it full screen.
Click through the scan and look at how a data flow from source to sink is reported.
Quick check
Does a system prompt prevent bad output?
No. It influences output but does not guarantee it. Validate in code.
Is this the same as prompt injection?
No. Prompt injection manipulates the model. Output handling is about what your code does with whatever comes back.
Sources
Last reviewed: 19 October 2026.
