CVE-2024-28734 (GCVE-0-2024-28734)
Vulnerability from cvelistv5 – Published: 2024-03-19 00:00 – Updated: 2024-08-02 00:56Summary
Cross Site Scripting vulnerability in Unit4 Financials by Coda prior to 2023Q4 allows a remote attacker to run arbitrary code via a crafted GET request using the cols parameter.
Severity
6.1 (Medium)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-06-10 19:44 UTC
CWE
- n/a
- CWE-79 - Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting')
Assigner
References
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| unit4 | financials |
Affected:
0 , < 2023Q4
(custom)
cpe:2.3:a:unit4:financials:*:*:*:*:*:*:*:* |
Previdian
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: d326fd3f-fd58-4b33-a2ac-e116b16aefba
Exploited: Yes
Timestamps
First Seen: 2024-03-19
Asserted: 2024-03-19
Scope
Notes: Cross Site Scripting vulnerability in Unit4 Financials by Coda prior to 2023Q4 allows a remote attacker to run arbitrary code via a crafted GET... | Affected: Unit4 / Unit4 Financials | CVSS: 6.1 (MEDIUM) | EPSS: 0.01791 | 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 | Cross Site Scripting vulnerability in Unit4 Financials by Coda prior to 2023Q4 allows a remote attacker to run arbitrary code via a crafted GET... |
| Cve Id | CVE-2024-28734 |
| Vendor | Unit4 |
| Ghsa Id | GHSA-WRFJ-MM2V-57MH |
| Product | Unit4 Financials |
| Added Date | 2024-03-19T00:00:00.000Z |
| Cvss Score | 6.1 |
| Epss Score | 0.01791 |
| Previous Ids | |
| Cvss Severity | MEDIUM |
| Virtual Patch | False |
| Cvss Estimated | False |
| Epss Percentile | 0.77569 |
| Used In Malware | unknown |
| Vulnerability Id | CVE-2024-28734 |
| Ahead Of Cisa Kev | None |
| Not Yet In Cisa Kev | True |
References
Created: 2026-10-02 09:11 CEST
| Updated: 2026-10-02 09:11 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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