CVE-2023-3722 (GCVE-0-2023-3722)
Vulnerability from cvelistv5 – Published: 2023-07-19 19:56 – Updated: 2024-10-22 19:17Title
Avaya Aura Device Services Remote Code Execution
Summary
An OS command injection vulnerability was found in the Avaya Aura Device Services Web application which could allow remote code execution as the Web server user via a malicious uploaded file. This issue affects Avaya Aura Device Services version 8.1.4.0 and earlier.
Severity
8.6 (High)
SSVC
Exploitation: none
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-10-22 19:16 UTC
CWE
- CWE-434 - Unrestricted Upload of File with Dangerous Type
Assigner
References
1 reference
Impacted products
2 products
| Vendor | Product | Version | |
|---|---|---|---|
| Avaya | Aura Device Services |
Affected:
0 , < 8.1.4.1
(custom)
|
|
| avaya | aura_device_services |
Affected:
0 , < 8.1.4.1
(custom)
cpe:2.3:a:avaya:aura_device_services:*:*:*:*:*:*:*:* |
Date Public
2023-07-18 21:00
Previdian
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: a650292f-b625-46ad-88dc-1a4ea19f8bb3
Exploited: Yes
Timestamps
First Seen: 2026-08-09
Asserted: 2026-08-09
Scope
Notes: Avaya Aura Device Services Remote Code Execution | Affected: Avaya / Aura Device Services | CVSS: 8.6 (HIGH) | EPSS: 0.0386 | 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 | Avaya Aura Device Services Remote Code Execution |
| Cve Id | CVE-2023-3722 |
| Vendor | Avaya |
| Ghsa Id | None |
| Product | Aura Device Services |
| Added Date | 2026-08-09T00:00:00.000Z |
| Cvss Score | 8.6 |
| Epss Score | 0.0386 |
| Previous Ids | |
| Cvss Severity | HIGH |
| Virtual Patch | False |
| Cvss Estimated | False |
| Epss Percentile | 0.8984 |
| Used In Malware | unknown |
| Vulnerability Id | CVE-2023-3722 |
| 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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