CWE-681 Detail

CWE-681

Incorrect Conversion between Numeric Types
High
Draft
2008-04-11
00h00 +00:00
2025-12-11
00h00 +00:00
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Name: Incorrect Conversion between Numeric Types

When converting from one data type to another, such as long to integer, data can be omitted or translated in a way that produces unexpected values. If the resulting values are used in a sensitive context, then dangerous behaviors may occur.

General Informations

Modes Of Introduction

Implementation

Applicable Platforms

Language

Name: C (Undetermined)
Class: Not Language-Specific (Undetermined)

Technologies

Class: Not Technology-Specific (Undetermined)

Common Consequences

Scope Impact Likelihood
Other
Integrity		Unexpected State, Quality Degradation

Note: The program could wind up using the wrong number and generate incorrect results. If the number is used to allocate resources or make a security decision, then this could introduce a vulnerability.

Observed Examples

References Description

CVE-2022-2639

Chain: integer coercion error (CWE-192) prevents a return value from indicating an error, leading to out-of-bounds write (CWE-787)

CVE-2021-43537

Chain: in a web browser, an unsigned 64-bit integer is forcibly cast to a 32-bit integer (CWE-681) and potentially leading to an integer overflow (CWE-190). If an integer overflow occurs, this can cause heap memory corruption (CWE-122)

CVE-2007-4268

Chain: integer signedness error (CWE-195) passes signed comparison, leading to heap overflow (CWE-122)

CVE-2007-4988

Chain: signed short width value in image processor is sign extended during conversion to unsigned int, which leads to integer overflow and heap-based buffer overflow.

CVE-2009-0231

Integer truncation of length value leads to heap-based buffer overflow.

CVE-2008-3282

Size of a particular type changes for 64-bit platforms, leading to an integer truncation in document processor causes incorrect index to be generated.

Potential Mitigations

Phases : Implementation
Avoid making conversion between numeric types. Always check for the allowed ranges.

Detection Methods

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.)
Effectiveness : High

Vulnerability Mapping Notes

Justification : 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.
Comment : 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.

References

REF-962

Automated Source Code Security Measure (ASCSM)
Object Management Group (OMG).
http://www.omg.org/spec/ASCSM/1.0/

Submission

Name Organization Date Date release Version
CWE Community 2008-04-11 +00:00 2008-04-11 +00:00 Draft 9

Modifications

Name Organization Date Comment
Sean Eidemiller Cigital 2008-07-01 +00:00 added/updated demonstrative examples
Eric Dalci Cigital 2008-07-01 +00:00 updated Potential_Mitigations, Time_of_Introduction
CWE Content Team MITRE 2008-09-08 +00:00 updated Relationships
CWE Content Team MITRE 2008-11-24 +00:00 updated Description, Relationships, Taxonomy_Mappings
CWE Content Team MITRE 2009-12-28 +00:00 updated Applicable_Platforms, Likelihood_of_Exploit, Potential_Mitigations
CWE Content Team MITRE 2010-02-16 +00:00 updated Relationships
CWE Content Team MITRE 2011-03-29 +00:00 updated Demonstrative_Examples
CWE Content Team MITRE 2011-06-01 +00:00 updated Common_Consequences, Relationships, Taxonomy_Mappings
CWE Content Team MITRE 2011-06-27 +00:00 updated Common_Consequences, Observed_Examples, Relationships
CWE Content Team MITRE 2011-09-13 +00:00 updated Relationships, Taxonomy_Mappings
CWE Content Team MITRE 2012-05-11 +00:00 updated Demonstrative_Examples, References, Relationships, Taxonomy_Mappings
CWE Content Team MITRE 2014-07-30 +00:00 updated Relationships, Taxonomy_Mappings
CWE Content Team MITRE 2017-11-08 +00:00 updated Likelihood_of_Exploit, Observed_Examples, Taxonomy_Mappings, Type
CWE Content Team MITRE 2019-01-03 +00:00 updated References, Relationships, Taxonomy_Mappings
CWE Content Team MITRE 2019-06-20 +00:00 updated Relationships, Type
CWE Content Team MITRE 2020-02-24 +00:00 updated Relationships
CWE Content Team MITRE 2020-08-20 +00:00 updated Relationships
CWE Content Team MITRE 2020-12-10 +00:00 updated Relationships
CWE Content Team MITRE 2021-03-15 +00:00 updated Relationships
CWE Content Team MITRE 2023-04-27 +00:00 updated Relationships
CWE Content Team MITRE 2023-06-29 +00:00 updated Mapping_Notes
CWE Content Team MITRE 2023-10-26 +00:00 updated Observed_Examples
CWE Content Team MITRE 2024-02-29 +00:00 updated Observed_Examples
CWE Content Team MITRE 2025-04-03 +00:00 updated Applicable_Platforms
CWE Content Team MITRE 2025-12-11 +00:00 updated Applicable_Platforms, Detection_Factors, Weakness_Ordinalities
The information presented on CVE Find originates from several carefully selected reference sources. CVE data is provided by MITRE Corporation and the National Vulnerability Database (NVD). The Known Exploited Vulnerabilities (KEV) catalog is sourced from the Cybersecurity and Infrastructure Security Agency (CISA), while EPSS scores come from FIRST.org. Additionally, data regarding software weaknesses (CWE) and common attack patterns (CAPEC) is maintained by MITRE Corporation, and information on hardware and software configurations (CPE) is provided by the NVD.