CVE-2023-1454 (GCVE-0-2023-1454)
Vulnerability from cvelistv5 – Published: 2023-03-17 06:56 – Updated: 2024-08-02 05:49Title
jeecg-boot qurestSql sql injection
Summary
A vulnerability classified as critical has been found in jeecg-boot 3.5.0. This affects an unknown part of the file jmreport/qurestSql. The manipulation of the argument apiSelectId leads to sql injection. It is possible to initiate the attack remotely. The exploit has been disclosed to the public and may be used. The associated identifier of this vulnerability is VDB-223299.
Severity
6.3 (Medium)
6.3 (Medium)
CWE
- CWE-89 - SQL Injection
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://vuldb.com/?id.223299 | vdb-entrytechnical-description |
| https://vuldb.com/?ctiid.223299 | signaturepermissions-required |
| https://github.com/J0hnWalker/jeecg-boot-sqli | broken-linkexploit |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| n/a | jeecg-boot |
Affected:
3.5.0
|
Previdian
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: 0bd0da1f-38dd-4f79-8f50-7674ae94f149
Exploited: Yes
Timestamps
First Seen: 2025-07-07
Asserted: 2025-07-07
Scope
Notes: jeecg-boot qurestSql sql injection | Affected: Jeecg / jeecg-boot | CVSS: 6.3 (MEDIUM) | EPSS: 0.35825 | 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 | jeecg-boot qurestSql sql injection |
| Cve Id | CVE-2023-1454 |
| Vendor | Jeecg |
| Ghsa Id | GHSA-J72F-4HGP-3MWC |
| Product | jeecg-boot |
| Added Date | 2025-07-07T00:00:00.000Z |
| Cvss Score | 6.3 |
| Epss Score | 0.35825 |
| Previous Ids | |
| Cvss Severity | MEDIUM |
| Virtual Patch | False |
| Cvss Estimated | False |
| Epss Percentile | 0.98424 |
| Used In Malware | unknown |
| Vulnerability Id | CVE-2023-1454 |
| 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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