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C library functions like gets should never be used since the maximum size of the input buffer is not passed as an argument. C library functions like scanf can be used safely, but require the programmer to take care with the selection of safe format strings, by sanitizing it before using it.
Translation validation can be used even with a compiler that sometimes generates incorrect code, as long as this incorrect does not manifest itself for a given program. Depending on the input program the translation validation can fail (because the generated code is wrong or the translation validation technique is too weak to show correctness).
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]
Following the flow of data between all the components of an application or group of applications allows validation of required calls to dedicated procedures for sanitization and that proper actions are taken to taint data in specific pieces of code. [12] [13]
If data sanitization methods are unsuccessful at removing all sensitive information, it poses the risk of leaking this information to attackers. [26] Numerous studies have been conducted to optimize ways of preserving sensitive information. Some data sanitization methods have a high sensitivity to distinct points that have no closeness to data ...
Validating or "sanitizing" input, such as whitelisting known good values. This can be done on the client side, which is prone to modification by malicious users, or on the server side, which is more secure. Encoding input or escaping dangerous characters.
Data cleaning differs from data validation in that validation almost invariably means data is rejected from the system at entry and is performed at the time of entry, rather than on batches of data. The actual process of data cleansing may involve removing typographical errors or validating and correcting values against a known list of entities.