CVE-2022-2487 (GCVE-0-2022-2487)
Vulnerability from cvelistv5 – Published: 2022-07-20 11:35 – Updated: 2025-04-15 14:04Title
WAVLINK WN535K2/WN535K3 nightled.cgi os command injection
Summary
A vulnerability has been found in WAVLINK WN535K2 and WN535K3 and classified as critical. This vulnerability affects unknown code of the file /cgi-bin/nightled.cgi. The manipulation of the argument start_hour leads to os command injection. The exploit has been disclosed to the public and may be used.
Severity
SSVC
Exploitation: poc
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-04-14 16:55 UTC
CWE
- CWE-78 - OS Command Injection
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/1angx/webray.com.cn/blob/main/… | x_refsource_MISC |
| https://vuldb.com/?id.204538 | x_refsource_MISC |
Impacted products
Previdian
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: 5553775a-bc40-4c38-ad70-ce47c3454b19
Exploited: Yes
Timestamps
First Seen: 2025-06-05
Asserted: 2025-06-05
Scope
Notes: WAVLINK WN535K2/WN535K3 nightled.cgi os command injection | Affected: WAVLINK / WN535K2, WN535K3 | CVSS: 8.0 (HIGH) | EPSS: 0.79513 | 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 | WAVLINK WN535K2/WN535K3 nightled.cgi os command injection |
| Cve Id | CVE-2022-2487 |
| Vendor | WAVLINK |
| Ghsa Id | None |
| Product | WN535K2, WN535K3 |
| Added Date | 2025-06-05T09:08:19.477Z |
| Cvss Score | 8.0 |
| Epss Score | 0.79513 |
| Previous Ids | |
| Cvss Severity | HIGH |
| Virtual Patch | False |
| Cvss Estimated | False |
| Epss Percentile | 0.99595 |
| Used In Malware | unknown |
| Vulnerability Id | CVE-2022-2487 |
| 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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