Preface: Self-service AI is a technology that uses AI techniques (such as chatbots, natural language processing (NLP), and machine learning) to enable customers to solve problems or find information themselves anytime, anywhere, without interacting with human customer service. It acts as a digital agent, providing instant assistance through channels such as websites, instant messaging applications, and voice systems.
Background: In the context of Amazon Content Designer, the result of using static-eval depends entirely on the AST (Abstract Syntax Tree) generated from your string and the Context (variables) you provide. The primary goal of static-eval is to return a plain JavaScript value without using the dangerous eval() function.
An Abstract Syntax Tree (AST) in the context of static evaluation (static-eval) is a hierarchical, tree-structured representation of source code that captures its logical and structural meaning without requiring the code to be executed.
In static analysis, the code is parsed into this tree format, allowing tools to traverse, analyze, and manipulate the structure to find potential bugs, security vulnerabilities, or styling issues before runtime.
Vulnerability details: Improper use of the static-eval npm package in the open source solution qnabot-on-aws versions 7.2.4 and earlier may allow an authenticated administrator to execute arbitrary code within the fulfillment Lambda execution context by injecting a crafted conditional chaining expression via the Content Designer interface, which bypasses the intended expression sandbox through JavaScript prototype manipulation. This may grant direct access to backend resources (Lambda environment variables, OpenSearch indices, S3 objects, DynamoDB tables) that are not exposed through normal administrative interfaces. We recommend you upgrade to version 7.3.0 or above.
Official announcement: Please refer to link for details –