CVE-2018-19276 (GCVE-0-2018-19276)
Vulnerability from cvelistv5 – Published: 2019-03-17 21:30 – Updated: 2024-08-05 11:30Summary
OpenMRS before 2.24.0 is affected by an Insecure Object Deserialization vulnerability that allows an unauthenticated user to execute arbitrary commands on the targeted system via crafted XML data in a request body.
Severity
10 (Critical)
CWE
- n/a
Assigner
References
5 references
| URL | Tags |
|---|---|
| http://packetstormsecurity.com/files/151553/OpenM… | x_refsource_MISC |
| https://www.exploit-db.com/exploits/46327/ | exploitx_refsource_EXPLOIT-DB |
| http://packetstormsecurity.com/files/155691/OpenM… | x_refsource_MISC |
| https://know.bishopfox.com/advisories/news/2019/0… | x_refsource_MISC |
| https://talk.openmrs.org/t/critical-security-advi… | x_refsource_CONFIRM |
Date Public
2019-02-05 00:00
Previdian
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: dfde07f3-ad40-47ac-b83a-a7724755270f
Exploited: Yes
Timestamps
First Seen: 2025-06-21
Asserted: 2025-06-21
Scope
Notes: OpenMRS before 2.24.0 is affected by an Insecure Object Deserialization vulnerability that allows an unauthenticated user to execute arbitrary... | Affected: OpenMRS / OpenMRS | CVSS: 9.8 (CRITICAL) | EPSS: 0.98714 | Used in malware: unknown | Not yet in CISA KEV: True
Evidence
Type: Public Report
Signal: Successful Exploitation
Confidence: 70%
Source: previdian
Details
| Feed | Previdian (previdian.com) |
|---|---|
| Title | OpenMRS before 2.24.0 is affected by an Insecure Object Deserialization vulnerability that allows an unauthenticated user to execute arbitrary... |
| Cve Id | CVE-2018-19276 |
| Vendor | OpenMRS |
| Ghsa Id | None |
| Product | OpenMRS |
| Added Date | 2025-06-21T00:00:00.000Z |
| Cvss Score | 9.8 |
| Epss Score | 0.98714 |
| Previous Ids | |
| Cvss Severity | CRITICAL |
| Virtual Patch | False |
| Cvss Estimated | False |
| Epss Percentile | 0.99924 |
| Used In Malware | unknown |
| Vulnerability Id | CVE-2018-19276 |
| Ahead Of Cisa Kev | None |
| Not Yet In Cisa Kev | True |
References
Created: 2026-10-02 09:09 CEST
| Updated: 2026-10-02 09:09 CEST
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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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