CWE-112 Detail

CWE-112

Missing XML Validation
Draft
2006-07-19
00h00 +00:00
2025-12-11
00h00 +00:00
CWE Mitre.org
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Gestione notifiche

Nome: Missing XML Validation

The product accepts XML from an untrusted source but does not validate the XML against the proper schema.

CWE Description

Most successful attacks begin with a violation of the programmer's assumptions. By accepting an XML document without validating it against a DTD or XML schema, the programmer leaves a door open for attackers to provide unexpected, unreasonable, or malicious input.

General Informations

Modes Of Introduction

Implementation

Piattaforme applicabili

Linguaggio

Class: Not Language-Specific (Undetermined)

Conseguenze comuni

Ambito Impatto Probabilità
IntegrityUnexpected State

Potential Mitigations

Phases : Architecture and Design

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

Note sulla mappatura delle vulnerabilità

Giustificazione : 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.
Commento : 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.

Pattern di attacco correlati

CAPEC-ID Nome del pattern di attacco
CAPEC-230 Serialized Data with Nested Payloads
Applications often need to transform data in and out of a data format (e.g., XML and YAML) by using a parser. It may be possible for an adversary to inject data that may have an adverse effect on the parser when it is being processed. Many data format languages allow the definition of macro-like structures that can be used to simplify the creation of complex structures. By nesting these structures, causing the data to be repeatedly substituted, an adversary can cause the parser to consume more resources while processing, causing excessive memory consumption and CPU utilization.
CAPEC-231 Oversized Serialized Data Payloads
An adversary injects oversized serialized data payloads into a parser during data processing to produce adverse effects upon the parser such as exhausting system resources and arbitrary code execution.

Riferimenti

REF-6

Seven Pernicious Kingdoms: A Taxonomy of Software Security Errors
Katrina Tsipenyuk, Brian Chess, Gary McGraw.
https://samate.nist.gov/SSATTM_Content/papers/Seven%20Pernicious%20Kingdoms%20-%20Taxonomy%20of%20Sw%20Security%20Errors%20-%20Tsipenyuk%20-%20Chess%20-%20McGraw.pdf

Invio

Nome Organizzazione Data Data di rilascio Version
7 Pernicious Kingdoms 2006-07-19 +00:00 2006-07-19 +00:00 Draft 3

Modifiche

Nome Organizzazione Data Commento
Sean Eidemiller Cigital 2008-07-01 +00:00 added/updated demonstrative examples
Eric Dalci Cigital 2008-07-01 +00:00 updated Demonstrative_Example, Potential_Mitigations, Time_of_Introduction
CWE Content Team MITRE 2008-09-08 +00:00 updated Relationships, Other_Notes, Taxonomy_Mappings, Weakness_Ordinalities
CWE Content Team MITRE 2008-11-24 +00:00 updated Description, Other_Notes
CWE Content Team MITRE 2009-05-27 +00:00 updated Demonstrative_Examples
CWE Content Team MITRE 2009-10-29 +00:00 updated Description
CWE Content Team MITRE 2011-06-01 +00:00 updated Common_Consequences
CWE Content Team MITRE 2011-06-27 +00:00 updated Common_Consequences
CWE Content Team MITRE 2012-05-11 +00:00 updated Relationships
CWE Content Team MITRE 2013-02-21 +00:00 updated Potential_Mitigations
CWE Content Team MITRE 2014-02-18 +00:00 updated Related_Attack_Patterns
CWE Content Team MITRE 2014-06-23 +00:00 updated Demonstrative_Examples, Other_Notes, Potential_Mitigations
CWE Content Team MITRE 2014-07-30 +00:00 updated Demonstrative_Examples, Relationships, Taxonomy_Mappings
CWE Content Team MITRE 2017-01-19 +00:00 updated Relationships
CWE Content Team MITRE 2017-11-08 +00:00 updated Applicable_Platforms, Causal_Nature, Relationships
CWE Content Team MITRE 2019-01-03 +00:00 updated Relationships
CWE Content Team MITRE 2020-02-24 +00:00 updated References, Related_Attack_Patterns, Relationships
CWE Content Team MITRE 2020-06-25 +00:00 updated Relationships
CWE Content Team MITRE 2023-01-31 +00:00 updated Description
CWE Content Team MITRE 2023-04-27 +00:00 updated Detection_Factors, Relationships
CWE Content Team MITRE 2023-06-29 +00:00 updated Mapping_Notes
CWE Content Team MITRE 2025-12-11 +00:00 updated Relationships
Le informazioni presentate su CVE Find provengono da diverse fonti di riferimento attentamente selezionate. I dati CVE sono forniti da MITRE Corporation e dal National Vulnerability Database (NVD). Il catalogo delle vulnerabilità sfruttate conosciute (KEV) proviene dalla Cybersecurity and Infrastructure Security Agency (CISA), mentre i punteggi EPSS provengono da FIRST.org. Inoltre, i dati relativi alle vulnerabilità software (CWE) e ai pattern di attacco comuni (CAPEC) sono mantenuti da MITRE Corporation, e le informazioni sulle configurazioni hardware e software (CPE) sono fornite da NVD.