Implementing security measures for LLM-powered coding
As we integrate LLMs into our development workflows, it’s crucial to implement robust security measures. These measures will help ensure that our LLM-assisted code is ready for real-world deployment. Let’s explore key areas of focus and practical steps to enhance security in LLM-powered coding environments.
Here are seven measures that should be taken to get more secure code.
Input sanitization and validation
When using LLMs for code generation or completion, it’s important to sanitize and validate all inputs, both those provided to the LLM and those generated by it.
Validation is where the data is checked to make sure it’s correct/accurate before processing or using it. Sanitization is where the data is cleaned, where parts that could be dangerous are removed or changed enough that they’re not dangerous [NinjaOne, Informatica].
Before passing any input to an LLM, validate it against...
