Insecure Output Handling
Insufficient validation, sanitization, and handling of LLM outputs before they are passed downstream — effectively giving users indirect access to extra functionality.
What it is
Insecure Output Handling refers to insufficient validation, sanitization, and handling of the outputs generated by LLMs before they are passed downstream to other components and systems. Because LLM-generated content can be controlled by prompt input, this is similar to giving users indirect access to additional functionality.
It differs from Overreliance, which concerns over-dependence on the accuracy and appropriateness of outputs; here the focus is what happens to outputs *before* they reach other systems.
Successful exploitation can result in XSS and CSRF in web browsers, as well as SSRF, privilege escalation, or remote code execution on backend systems.
Impact increases when the application grants the LLM privileges beyond those of end users, when it is vulnerable to indirect prompt injection, or when third-party plugins inadequately validate inputs.
Common examples
- LLM output is entered directly into a system shell or a function such as exec or eval, resulting in remote code execution.
- JavaScript or Markdown generated by the LLM is returned to the user and interpreted by the browser, resulting in XSS.
Prevention & mitigation
- Treat the model like any other user: adopt a zero-trust approach and apply proper input validation on responses coming from the model to backend functions.
- Follow the OWASP ASVS (Application Security Verification Standard) guidelines for effective input validation and sanitization.
- Encode model output back to users to mitigate undesired code execution by JavaScript or Markdown (OWASP ASVS provides output-encoding guidance).
Example attack scenarios
- An LLM plugin's response is passed without output validation to a privileged administrative function, causing the plugin to shut down for maintenance.
- A website-summarizer tool is fed a prompt injection that captures sensitive content and, without output filtering, encodes and sends it to an attacker-controlled server.
- An LLM builds SQL from a chat-like feature; an unscrutinized “delete all tables” query destroys the database.
- A web app generates content from prompts without output sanitization; a crafted prompt returns an unsanitized JavaScript payload, causing XSS in the victim's browser.
References
- Arbitrary Code Execution: Snyk Security Blog
- ChatGPT Plugin Exploit Explained – From Prompt Injection to Accessing Private Data: Embrace The Red
- New prompt injection attack on ChatGPT web version — Markdown images can steal your chat data: System Weakness
- Don't blindly trust LLM responses — Threats to chatbots: Embrace The Red
- OWASP ASVS – 5 Validation, Sanitization and Encoding: OWASP
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