CVE-2023-6474 (GCVE-0-2023-6474)
Vulnerability from cvelistv5 – Published: 2023-12-02 23:31 – Updated: 2025-06-03 02:18
VLAI
EPSS
VEX
Title
PHPGurukul Nipah Virus Testing Management System manage-phlebotomist.php cross-site request forgery
Summary
A vulnerability has been found in PHPGurukul Nipah Virus Testing Management System 1.0 and classified as problematic. This vulnerability affects unknown code of the file manage-phlebotomist.php. The manipulation of the argument pid leads to cross-site request forgery. The attack can be initiated remotely. The exploit has been disclosed to the public and may be used. The identifier of this vulnerability is VDB-246640.
Severity
4.3 (Medium)
4.3 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-06-03 02:17 UTC
CWE
- CWE-352 - Cross-Site Request Forgery
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://vuldb.com/?id.246640 | vdb-entrytechnical-description |
| https://vuldb.com/?ctiid.246640 | signaturepermissions-required |
| https://github.com/dhabaleshwar/niv_testing_csrf/… | exploit |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| PHPGurukul | Nipah Virus Testing Management System |
Affected:
1.0
|
guessed |
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"time": "2023-12-02T00:00:00.000Z",
"value": "Advisory disclosed"
},
{
"lang": "en",
"time": "2023-12-02T01:00:00.000Z",
"value": "VulDB entry created"
},
{
"lang": "en",
"time": "2023-12-02T08:39:43.000Z",
"value": "VulDB entry last update"
}
],
"title": "PHPGurukul Nipah Virus Testing Management System manage-phlebotomist.php cross-site request forgery"
}
},
"cveMetadata": {
"assignerOrgId": "1af790b2-7ee1-4545-860a-a788eba489b5",
"assignerShortName": "VulDB",
"cveId": "CVE-2023-6474",
"datePublished": "2023-12-02T23:31:04.514Z",
"dateReserved": "2023-12-02T07:34:35.254Z",
"dateUpdated": "2025-06-03T02:18:00.896Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.1"
}
}
}
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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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