CVE-2021-26558 (GCVE-0-2021-26558)
Vulnerability from cvelistv5 – Published: 2021-11-11 09:35 – Updated: 2024-08-03 20:26
VLAI
EPSS
VEX
Title
Deserialization of Untrusted Data
Summary
Deserialization of Untrusted Data vulnerability of Apache ShardingSphere-UI allows an attacker to inject outer link resources. This issue affects Apache ShardingSphere-UI Apache ShardingSphere-UI version 4.1.1 and later versions; Apache ShardingSphere-UI versions prior to 5.0.0.
Severity
No CVSS data available.
CWE
- CWE-502 - Deserialization of Untrusted Data
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://lists.apache.org/thread/4gzkm1zb6c97v9gl8… | x_refsource_MISC |
| http://www.openwall.com/lists/oss-security/2021/11/11/1 | mailing-listx_refsource_MLIST |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Apache Software Foundation | Apache ShardingSphere-UI |
Affected:
4.1.1 , < Apache ShardingSphere-UI*
(custom)
|
guessed |
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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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