CVE-2022-34169 (GCVE-0-2022-34169)
Vulnerability from cvelistv5 – Published: 2022-07-19 00:00 – Updated: 2026-05-27 12:51
VLAI
EPSS
VEX
Title
Apache Xalan Java XSLT library is vulnerable to an integer truncation issue when processing malicious XSLT stylesheets
Summary
The Apache Xalan Java XSLT library is vulnerable to an integer truncation issue when processing malicious XSLT stylesheets. This can be used to corrupt Java class files generated by the internal XSLTC compiler and execute arbitrary Java bytecode. Users are recommended to update to version 2.7.3 or later. Note: Java runtimes (such as OpenJDK) include repackaged copies of Xalan.
Severity
7.5 (High)
SSVC
Exploitation: none
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-05-20 16:24 UTC
CWE
- integer truncation
- CWE-681 - Incorrect Conversion between Numeric Types
Assigner
References
24 references
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Apache Software Foundation | Apache Xalan-J |
Affected:
Xalan-J , ≤ 2.7.2
(custom)
|
guessed |
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"value": "The Apache Xalan Java XSLT library is vulnerable to an integer truncation issue when processing malicious XSLT stylesheets. This can be used to corrupt Java class files generated by the internal XSLTC compiler and execute arbitrary Java bytecode. Users are recommended to update to version 2.7.3 or later. Note: Java runtimes (such as OpenJDK) include repackaged copies of Xalan."
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"value": "La biblioteca Apache Xalan Java XSLT es vulnerable a un problema de truncamiento de enteros cuando procesa hojas de estilo XSLT maliciosas. Esto puede usarse para corromper los archivos de clase Java generados por el compilador interno XSLTC y ejecutar c\u00f3digo de bytes Java arbitrario. El proyecto Apache Xalan Java est\u00e1 inactivo y en proceso de ser retirado. No son esperadas futuras versiones de Apache Xalan Java que abordan este problema. Nota: Los tiempos de ejecuci\u00f3n de Java (como OpenJDK) incluyen copias reempaquetadas de Xalan."
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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