Improper Output Handling in AI LLM
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Description
Improper Output Handling occurs when outputs from large language models (LLMs) aren’t properly validated, sanitized, or managed before being passed to other parts of a system.
This kind of oversight can lead to serious problems with security, functionality, and the overall user experience. The impact often depends on how the model is set up and what it connects with, such as browsers, databases, logging tools, or operating systems.
Impact
If attackers successfully exploit these vulnerabilities, they could cause a wide range of security issues. These might include client-side flaws like Cross-Site Scripting (XSS) or Cross-Site Request Forgery (CSRF), as well as backend threats like Server-Side Request Forgery (SSRF) or even remote code execution.
Scenarios
Take a shared chat platform, for example, where multiple users interact with an AI assistant live.
If there’s no proper content sanitization in place, an attacker could submit a prompt that causes the AI to output a response containing malicious JavaScript. This is a classic reflected XSS attack. Once the assistant displays the response, the script runs in the browsers of everyone viewing the chat.
Without output encoding or escaping, the malicious script could steal session cookies, capture user data, or perform actions on behalf of users, all without their knowledge.
Prevention
It’s critical to apply rigorous, context-aware checks and sanitize any output from LLMs. Outputs from these models should be treated like untrusted data, similar to how user inputs are handled in secure coding.
Make sure the sanitization fits where the output will go:
- Web browsers: Encode outputs to block XSS attacks, so any HTML or JavaScript can’t run.
- Databases: Sanitize outputs to prevent SQL Injection attacks by using prepared statements or parameterized queries.
- Command-line: Validate and escape outputs to avoid OS Command Injections.