VAR-201804-0750
Vulnerability from variot - Updated: 2023-12-18 13:33IBM WebSphere DataPower Appliances 7.1, 7.2, 7.5, 7.5.1, 7.5.2, and 7.6 is vulnerable to a XML External Entity Injection (XXE) attack when processing XML data. A remote attacker could exploit this vulnerability to expose sensitive information or consume memory resources. IBM X-Force ID: 139023. Vendors have confirmed this vulnerability IBM X-Force ID: 139023 It is released as.Information is obtained and service operation is interrupted (DoS) There is a possibility of being put into a state. IBM DataPower Gateways 7.1.0.0 through 7.1.0.21, 7.2.0.0 through 7.2.0.18, 7.5.0.0 through 7.5.0.13, 7.5.1.0 through 7.5.1.12, 7.5.2.0 through 7.5.2.12 and 7.6.0.0 through 7.6.0.5 are vulnerable; other versions may also be affected. The appliance is primarily used to simplify, secure and accelerate XML and Web services deployment in SOA. The following versions are affected: IBM WebSphere DataPower Appliances Version 7.1, Version 7.2, Version 7.5, Version 7.5.1, Version 7.5.2, Version 7.6
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"data": "IBM WebSphere DataPower Appliances 7.1, 7.2, 7.5, 7.5.1, 7.5.2, and 7.6 is vulnerable to a XML External Entity Injection (XXE) attack when processing XML data. A remote attacker could exploit this vulnerability to expose sensitive information or consume memory resources. IBM X-Force ID: 139023. Vendors have confirmed this vulnerability IBM X-Force ID: 139023 It is released as.Information is obtained and service operation is interrupted (DoS) There is a possibility of being put into a state. \nIBM DataPower Gateways 7.1.0.0 through 7.1.0.21, 7.2.0.0 through 7.2.0.18, 7.5.0.0 through 7.5.0.13, 7.5.1.0 through 7.5.1.12, 7.5.2.0 through 7.5.2.12 and 7.6.0.0 through 7.6.0.5 are vulnerable; other versions may also be affected. The appliance is primarily used to simplify, secure and accelerate XML and Web services deployment in SOA. The following versions are affected: IBM WebSphere DataPower Appliances Version 7.1, Version 7.2, Version 7.5, Version 7.5.1, Version 7.5.2, Version 7.6",
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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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