CVE-2023-6607 (GCVE-0-2023-6607)
Vulnerability from cvelistv5 – Published: 2023-12-08 14:00 – Updated: 2024-08-02 08:35
VLAI
EPSS
VEX
Title
Tongda OA 2017 delete.php sql injection
Summary
A vulnerability has been found in Tongda OA 2017 up to 11.10 and classified as critical. Affected by this vulnerability is an unknown functionality of the file general/wiki/cp/manage/delete.php. The manipulation of the argument TERM_ID_STR leads to sql injection. The exploit has been disclosed to the public and may be used. The associated identifier of this vulnerability is VDB-247243. NOTE: The vendor was contacted early about this disclosure but did not respond in any way.
Severity
5.5 (Medium)
5.5 (Medium)
CWE
- CWE-89 - SQL Injection
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://vuldb.com/?id.247243 | vdb-entrytechnical-description |
| https://vuldb.com/?ctiid.247243 | signaturepermissions-required |
| https://github.com/willchen0011/cve/blob/main/sql.md | exploit |
Impacted products
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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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