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User input (including an XSS vector) would be sent to the server, and then sent back to the user as a web page. The need for an improved user experience resulted in popularity of applications that had a majority of the presentation logic (maybe written in JavaScript) working on the client-side that pulled data, on-demand, from the server using ...
Improper input validation [1] or unchecked user input is a type of vulnerability in computer software that may be used for security exploits. [2] This vulnerability is caused when "[t]he product does not validate or incorrectly validates input that can affect the control flow or data flow of a program." [1] Examples include: Buffer overflow
Data validation is intended to provide certain well-defined guarantees for fitness and consistency of data in an application or automated system. Data validation rules can be defined and designed using various methodologies, and be deployed in various contexts. [1]
Code injection is a computer security exploit where a program fails to correctly process external data, such as user input, causing it to interpret the data as executable commands. An attacker using this method "injects" code into the program while it is running.
Software validation ensures that "you built the right thing" and confirms that the product, as provided, fulfills the intended use and goals of the stakeholders. This article has used the strict or narrow definition of verification. From a testing perspective: Fault – wrong or missing function in the code.
In data sanitization, HTML sanitization is the process of examining an HTML document and producing a new HTML document that preserves only whatever tags and attributes are designated "safe" and desired. HTML sanitization can be used to protect against attacks such as cross-site scripting (XSS) by sanitizing any HTML code submitted by a user.
Secret-Restricted Data Cover Sheet, By Glunggenbauer, Shared under CC BY 2.0 Wikimedia. Data sanitization policy must be comprehensive and include data levels and correlating sanitization methods. Any data sanitization policy created must be comprehensive and include all forms of media to include soft and hard copy data.
Data cleansing or data cleaning is the process of identifying and correcting (or removing) corrupt, inaccurate, or irrelevant records from a dataset, table, or database.It involves detecting incomplete, incorrect, or inaccurate parts of the data and then replacing, modifying, or deleting the affected data. [1]