CVE-2020-26879 (GCVE-0-2020-26879)
Vulnerability from cvelistv5 – Published: 2020-10-26 19:20 – Updated: 2024-08-04 16:03Summary
Ruckus vRioT through 1.5.1.0.21 has an API backdoor that is hardcoded into validate_token.py. An unauthenticated attacker can interact with the service API by using a backdoor value as the Authorization header.
Severity
9.8 (Critical)
CWE
- n/a
Assigner
References
6 references
| URL | Tags |
|---|---|
| https://adepts.of0x.cc | x_refsource_MISC |
| https://twitter.com/TheXC3LL | x_refsource_MISC |
| https://x-c3ll.github.io | x_refsource_MISC |
| https://support.ruckuswireless.com/documents | x_refsource_MISC |
| https://support.ruckuswireless.com/security_bulle… | x_refsource_CONFIRM |
| https://adepts.of0x.cc/ruckus-vriot-rce/ | x_refsource_MISC |
Previdian
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: 125cb652-371b-4aa0-a4c3-c11fae3f8e4f
Exploited: Yes
Timestamps
First Seen: 2026-01-04
Asserted: 2026-01-04
Scope
Notes: Ruckus vRioT through 1.5.1.0.21 has an API backdoor that is hardcoded into validate_token.py. An unauthenticated attacker can interact with the... | Affected: Ruckus Wireless / vRioT | CVSS: 9.8 (CRITICAL) | EPSS: 0.45083 | 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 | Ruckus vRioT through 1.5.1.0.21 has an API backdoor that is hardcoded into validate_token.py. An unauthenticated attacker can interact with the... |
| Cve Id | CVE-2020-26879 |
| Vendor | Ruckus Wireless |
| Ghsa Id | None |
| Product | vRioT |
| Added Date | 2026-01-04T00:00:00.000Z |
| Cvss Score | 9.8 |
| Epss Score | 0.45083 |
| Previous Ids | |
| Cvss Severity | CRITICAL |
| Virtual Patch | False |
| Cvss Estimated | False |
| Epss Percentile | 0.98746 |
| Used In Malware | unknown |
| Vulnerability Id | CVE-2020-26879 |
| 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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