CVE-2024-23624 (GCVE-0-2024-23624)
Vulnerability from cvelistv5 – Published: 2024-01-25 23:41 – Updated: 2025-06-17 21:19Title
D-Link DAP-1650 gena.cgi SUBSCRIBE Command Injection Vulnerability
Summary
A command injection vulnerability exists in the gena.cgi module of D-Link DAP-1650 devices. An unauthenticated attacker can exploit this vulnerability to gain command execution on the device as root.
Severity
9.6 (Critical)
SSVC
Exploitation: none
Automatable: yes
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-01-26 20:27 UTC
CWE
- CWE-77 - Improper Neutralization of Special Elements used in a Command ('Command Injection')
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://blog.exodusintel.com/2024/01/25/d-link-da… | third-party-advisory |
Impacted products
Previdian
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: 84e9cef2-d4a1-4690-bd9f-9e8be9e8372a
Exploited: Yes
Timestamps
First Seen: 2026-10-05
Asserted: 2026-10-05
Scope
Notes: D-Link DAP-1650 gena.cgi SUBSCRIBE Command Injection Vulnerability | Affected: D-Link / DAP-1650 | CVSS: 9.6 (CRITICAL) | EPSS: 0.25988 | 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 | D-Link DAP-1650 gena.cgi SUBSCRIBE Command Injection Vulnerability |
| Cve Id | CVE-2024-23624 |
| Vendor | D-Link |
| Ghsa Id | None |
| Product | DAP-1650 |
| Added Date | 2026-10-05T16:02:55.607Z |
| Cvss Score | 9.6 |
| Epss Score | 0.25988 |
| Previous Ids | |
| Cvss Severity | CRITICAL |
| Virtual Patch | False |
| Cvss Estimated | False |
| Epss Percentile | 0.97932 |
| Used In Malware | unknown |
| Vulnerability Id | CVE-2024-23624 |
| Ahead Of Cisa Kev | None |
| Not Yet In Cisa Kev | True |
References
Created: 2026-10-06 17:00 UTC
| Updated: 2026-10-06 17:00 UTC
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"dateReserved": "2024-01-18T21:37:19.591Z",
"dateUpdated": "2025-06-17T21:19:30.921Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.1"
}
}
}
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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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