CVE-2024-8877 (GCVE-0-2024-8877)
Vulnerability from cvelistv5 – Published: 2024-09-24 15:19 – Updated: 2025-11-04 16:16Title
SQL Injection
Summary
Improper neutralization of special elements results in a SQL Injection vulnerability in Riello Netman 204. It is only limited to the SQLite database of measurement data.This issue affects Netman 204: through 4.05.
Severity
SSVC
Exploitation: poc
Automatable: yes
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-09-27 15:21 UTC
CWE
- CWE-89 - Improper Neutralization of Special Elements used in an SQL Command ('SQL Injection')
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://cyberdanube.com/en/en-multiple-vulnerabil… | third-party-advisoryexploit |
| http://seclists.org/fulldisclosure/2024/Sep/50 |
Impacted products
2 products
| Vendor | Product | Version | |
|---|---|---|---|
| Riello | Netman 204 |
Affected:
0 , ≤ 4.05
(custom)
|
|
| riello-ups | netman_204_firmware |
Affected:
0 , ≤ 4.05
(custom)
cpe:2.3:o:riello-ups:netman_204_firmware:*:*:*:*:*:*:*:* |
Previdian
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: 5b40b6a4-2ec6-4e0b-b4ea-215e4f94b156
Exploited: Yes
Timestamps
First Seen: 2025-06-26
Asserted: 2025-06-26
Scope
Notes: SQL Injection | Affected: Riello / Netman 204 | CVSS: 6.9 (MEDIUM) | EPSS: 0.77265 | 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 | SQL Injection |
| Cve Id | CVE-2024-8877 |
| Vendor | Riello |
| Ghsa Id | None |
| Product | Netman 204 |
| Added Date | 2025-06-26T00:00:00.000Z |
| Cvss Score | 6.9 |
| Epss Score | 0.77265 |
| Previous Ids | |
| Cvss Severity | MEDIUM |
| Virtual Patch | False |
| Cvss Estimated | False |
| Epss Percentile | 0.99543 |
| Used In Malware | unknown |
| Vulnerability Id | CVE-2024-8877 |
| 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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