CVE-2024-0492 (GCVE-0-2024-0492)
Vulnerability from cvelistv5 – Published: 2024-01-13 15:00 – Updated: 2024-08-15 15:45
VLAI
EPSS
VEX
Title
Kashipara Billing Software HTTP POST Request buyer_detail_submit.php sql injection
Summary
A vulnerability classified as critical was found in Kashipara Billing Software 1.0. Affected by this vulnerability is an unknown functionality of the file buyer_detail_submit.php of the component HTTP POST Request Handler. The manipulation of the argument gstn_no leads to sql injection. The attack can be launched remotely. The exploit has been disclosed to the public and may be used. The identifier VDB-250597 was assigned to this vulnerability.
Severity
6.3 (Medium)
6.3 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-08-15 15:45 UTC
CWE
- CWE-89 - SQL Injection
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://vuldb.com/?id.250597 | vdb-entrytechnical-description |
| https://vuldb.com/?ctiid.250597 | signaturepermissions-required |
| https://github.com/laoquanshi/BILLING-SOFTWARE-SQ… | exploit |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Kashipara | Billing Software |
Affected:
1.0
|
guessed |
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"tags": [
"signature",
"permissions-required"
],
"url": "https://vuldb.com/?ctiid.250597"
},
{
"tags": [
"exploit"
],
"url": "https://github.com/laoquanshi/BILLING-SOFTWARE-SQL-injection-vulnerability/blob/main/BILLING%20SOFTWARE%20sql.docx"
}
],
"timeline": [
{
"lang": "en",
"time": "2024-01-12T00:00:00.000Z",
"value": "Advisory disclosed"
},
{
"lang": "en",
"time": "2024-01-12T01:00:00.000Z",
"value": "VulDB entry created"
},
{
"lang": "en",
"time": "2024-01-12T13:26:13.000Z",
"value": "VulDB entry last update"
}
],
"title": "Kashipara Billing Software HTTP POST Request buyer_detail_submit.php sql injection"
}
},
"cveMetadata": {
"assignerOrgId": "1af790b2-7ee1-4545-860a-a788eba489b5",
"assignerShortName": "VulDB",
"cveId": "CVE-2024-0492",
"datePublished": "2024-01-13T15:00:06.351Z",
"dateReserved": "2024-01-12T12:20:41.826Z",
"dateUpdated": "2024-08-15T15:45:49.014Z",
"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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