VAR-201907-1475
Vulnerability from variot - Updated: 2023-12-18 13:52The SAP Gateway, versions 7.5, 7.51, 7.52 and 7.53, allows an attacker to inject content which is displayed in the form of an error message. An attacker could thus mislead a user to believe this information is from the legitimate service when it's not. SAP Gateway Contains an injection vulnerability.Information may be altered. SAP Gateway is prone to a content injection vulnerability because the application fails to properly sanitize user-supplied input. Successful exploits will allow attacker-supplied content to be passed in context of the affected application ; Other attacks are also possible. The product supports non-SAP applications to connect to SAP applications, and can also connect and access SAP applications on mobile devices.
[VulnerabilityType Other] Content Spoofing
[Vendor of Product] SAP
[Affected Product] SAPUI5 1.0.0 and the SAP Gateway versions 7.5, 7.51, 7.52 and 7.53
[PoC] Tested in SAPUI5 1.0.0 PoC:
https://sapmobile.target.com/sap/opu/odata/UI2/INTEROP/PersContainers(category='P ',id='flp.settings.FlpSettings')?$expand=PersContainerItemsu1kpa_HACKED_&sap-cache-id=D49C673A8D0D275477C7CD1FBFA3EE31
[Attack Type] Remote
[Reference] https://capec.mitre.org/data/definitions/148.html https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-0319
[Discoverer] Offensive0Labs - Rafael Fontes Souza
References below: "SAP Product Security Response Team seg, 8 de jul 04:33 (há 6 dias) para eu, SAP
Hello Rafael,
We are pleased to inform you that we are releasing the following security note on July Patch Day 2019:
Sec Incident ID(s) 1870475251
Security Note 2752614
Security Note Title [CVE-2019-0319] Content Injection Vulnerability in SAP Gateway
Advisory Plan Date 10/09/2019
Delivery date of fix/Patch Day 07/09/2019
CVSS Base Score 4.3
CVSS Base Vector NLNR | U | NLN
Credits go to:
Offensive0Labs, Rafael Fontes Souza
*Notes will be visible to customers on 9th of July 2019.
https://wiki.scn.sap.com/wiki/display/PSR/Acknowledgments+to+Security+Researchers
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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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