CWE-197 詳細資料

Truncation errors occur when a primitive is cast to a primitive of a smaller size and data is lost in the conversion.

引入方式

Implementation : While truncation may be employed viably to isolate the low bits of a value, this usage is rare, and truncation usually implies that an implementation error has occurred.

適用平台

程式語言

名稱： C (Undetermined)
名稱： C++ (Undetermined)
名稱： Java (Undetermined)
名稱： C# (Undetermined)
名稱： Python (Undetermined)

常見後果

觀察到的案例

潛在緩解措施

Phases : Implementation
Ensure that no casts, implicit or explicit, take place that move from a larger size primitive or a smaller size primitive.

偵測方法

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.
有效性 : 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.)
有效性 : High

漏洞對應備注

理由說明 : This CWE entry is at the Base level of abstraction, which is a preferred level of abstraction for mapping to the root causes of vulnerabilities.
備註 : Carefully read both the name and description to ensure that this mapping is an appropriate fit. Do not try to 'force' a mapping to a lower-level Base/Variant simply to comply with this preferred level of abstraction.

備注

This weakness has traditionally been under-studied and under-reported, although vulnerabilities in popular software have been published in 2008 and 2009.

參考資料

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

REF-1541

Secure Coding Guide for Python
OpenSSF.
https://best.openssf.org/Secure-Coding-Guide-for-Python/

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