Detail CWE-682

The product performs a calculation that generates incorrect or unintended results that are later used in security-critical decisions or resource management.

Mode Pengenalan

Implementation

Platform yang Berlaku

Bahasa

Kelas: Not Language-Specific (Undetermined)

Teknologi

Kelas: Not Technology-Specific (Undetermined)

Konsekuensi Umum

Contoh yang Diamati

Mitigasi yang Memungkinkan

Phases : Implementation
Understand your programming language's underlying representation and how it interacts with numeric calculation. Pay close attention to byte size discrepancies, precision, signed/unsigned distinctions, truncation, conversion and casting between types, "not-a-number" calculations, and how your language handles numbers that are too large or too small for its underlying representation.
Phases : Implementation
Perform input validation on any numeric input by ensuring that it is within the expected range. Enforce that the input meets both the minimum and maximum requirements for the expected range.
Phases : Implementation
Use the appropriate type for the desired action. For example, in C/C++, only use unsigned types for values that could never be negative, such as height, width, or other numbers related to quantity.
Phases : Architecture and Design
Phases : Architecture and Design
Phases : Implementation
Examine compiler warnings closely and eliminate problems with potential security implications, such as signed / unsigned mismatch in memory operations, or use of uninitialized variables. Even if the weakness is rarely exploitable, a single failure may lead to the compromise of the entire system.

Metode Deteksi

Manual Analysis

Efektivitas : High

Automated Static Analysis

Automated static analysis, commonly referred to as Static Application Security Testing (SAST), can find some instances of this weakness by analyzing source code (or binary/compiled code) without having to execute it. Typically, this is done by building a model of data flow and control flow, then searching for potentially-vulnerable patterns that connect "sources" (origins of input) with "sinks" (destinations where the data interacts with external components, a lower layer such as the OS, etc.)

Fuzzing

Fuzz testing (fuzzing) is a powerful technique for generating large numbers of diverse inputs - either randomly or algorithmically - and dynamically invoking the code with those inputs. Even with random inputs, it is often capable of generating unexpected results such as crashes, memory corruption, or resource consumption. Fuzzing effectively produces repeatable test cases that clearly indicate bugs, which helps developers to diagnose the issues.
Efektivitas : High

Catatan Pemetaan Kerentanan

Justifikasi : This CWE entry is extremely high-level, a Pillar. In many cases, lower-level children or descendants are more appropriate. However, sometimes this weakness is forced to be used due to the lack of in-depth weakness research. See Research Gaps.
Komentar : Where feasible, consider children or descendants of this entry instead.

Pola Serangan Terkait

Catatan

Referensi

REF-106

SafeInt
David LeBlanc, Niels Dekker.
https://github.com/dcleblanc/SafeInt/

REF-44

24 Deadly Sins of Software Security
Michael Howard, David LeBlanc, John Viega.

REF-62

The Art of Software Security Assessment
Mark Dowd, John McDonald, Justin Schuh.

REF-1534

Automated Technical Debt Measure, Version 2
OMG.
https://www.omg.org/spec/ATDM2/1.0/PDF

Pengajuan

Modifikasi