CVE-2025-3987 (GCVE-0-2025-3987)
Vulnerability from cvelistv5 – Published: 2025-04-27 21:31 – Updated: 2025-04-28 17:28Title
TOTOLINK N150RT formWsc command injection
Summary
A vulnerability was found in TOTOLINK N150RT 3.4.0-B20190525. It has been rated as critical. This issue affects some unknown processing of the file /boafrm/formWsc. The manipulation of the argument localPin leads to command injection. The attack may be initiated remotely. The exploit has been disclosed to the public and may be used.
Severity
6.3 (Medium)
6.3 (Medium)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-04-28 17:25 UTC
Assigner
References
5 references
| URL | Tags |
|---|---|
| https://vuldb.com/?id.306323 | vdb-entrytechnical-description |
| https://vuldb.com/?ctiid.306323 | signaturepermissions-required |
| https://vuldb.com/?submit.557938 | third-party-advisory |
| https://github.com/fizz-is-on-the-way/Iot_vuls/tr… | exploit |
| https://www.totolink.net/ | product |
Previdian
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: 72b23191-4a84-4ce7-ad8f-49414ecae4be
Exploited: Yes
Timestamps
First Seen: 2025-04-27
Asserted: 2025-04-27
Scope
Notes: TOTOLINK N150RT formWsc command injection | Affected: TOTOLINK / N150RT | CVSS: 5.3 (MEDIUM) | EPSS: 0.10488 | 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 | TOTOLINK N150RT formWsc command injection |
| Cve Id | CVE-2025-3987 |
| Vendor | TOTOLINK |
| Ghsa Id | None |
| Product | N150RT |
| Added Date | 2025-04-27T21:31:06.000Z |
| Cvss Score | 5.3 |
| Epss Score | 0.10488 |
| Previous Ids | |
| Cvss Severity | MEDIUM |
| Virtual Patch | False |
| Cvss Estimated | False |
| Epss Percentile | 0.95627 |
| Used In Malware | unknown |
| Vulnerability Id | CVE-2025-3987 |
| Ahead Of Cisa Kev | None |
| Not Yet In Cisa Kev | True |
References
Created: 2026-10-02 09:10 CEST
| Updated: 2026-10-02 09:10 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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