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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
Example 1: legacy code may have been designed for ASCII input but now the input is UTF-8. Example 2 : legacy code may have been compiled and tested on 32-bit architectures, but when compiled on 64-bit architectures, new arithmetic problems may occur (e.g., invalid signedness tests, invalid type casts, etc.).
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).
As with any input validation process, it is important to ensure that the format is not restrictive in a way that unintentionally restricts the use of the application by some users. An example of this is restricting telephone number or postal code [ 6 ] input based on one country's system (e.g. requiring every user to give a five-digit ZIP code ...
A code sanitizer is a programming tool that detects bugs in the form of undefined or suspicious behavior by a compiler inserting instrumentation code at runtime. The class of tools was first introduced by Google's AddressSanitizer (or ASan) of 2012, which uses directly mapped shadow memory to detect memory corruption such as buffer overflows or accesses to a dangling pointer (use-after-free).
A software code audit is a comprehensive analysis of source code in a programming project with the intent of discovering bugs, security breaches or violations of programming conventions. It is an integral part of the defensive programming paradigm, which attempts to reduce errors before the software is released.
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In general, data sanitization techniques use algorithms to detect anomalies and remove any suspicious points that may be poisoned data or sensitive information. Furthermore, data sanitization methods may remove useful, non-sensitive information, which then renders the sanitized dataset less useful and altered from the original.