CVE-2026-80147 (GCVE-0-2026-80147)
Vulnerability from cvelistv5 – Published: 2026-09-22 15:20 – Updated: 2026-09-22 16:04
VLAI
EPSS
VEX
Title
Lantronix Autonomous Out-of-Band Devices Stack-Based Buffer Overflow via mfc eeprom write
Summary
Lantronix SLC8000 before firmware v9.7.0.2, EMG8500/EMG7500 before firmware v9.7.0.1, and all firmware versions of SLB882/SLCx-03/SLCx-02 contain a stack-based buffer overflow vulnerability that allows authenticated attackers to potentially execute arbitrary code by exploiting an undocumented mfc eeprom write command that copies unbounded user input into a bounded stack buffer before passing it to a system() call. Attackers can authenticate as any user to the terminal or CLI interface and supply an oversized input to trigger the overflow, potentially achieving complete loss of confidentiality, integrity, and availability on the affected device and impacting downstream serial-attached devices.
Severity
9.9 (Critical)
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-22 16:02 UTC
CWE
- CWE-121 - Stack-based Buffer Overflow
Assigner
References
5 references
| URL | Tags |
|---|---|
| https://revrb.net/2026/09/21/revrb-lantern.html | technical-description |
| https://ts.lantronix.com/ftp/slc8000/9.7.0.2R1/ | patch |
| https://ts.lantronix.com/ftp/emg/EMG_8500/9.7.0.1R2/ | patch |
| https://ts.lantronix.com/ftp/emg/EMG_7500/9.7.0.1R2/ | patch |
| https://www.vulncheck.com/advisories/lantronix-au… | third-party-advisory |
Impacted products
6 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| LANTRONIX | SLC8000 |
Affected:
0 , < 9.7.0.2
(custom)
|
guessed | |
| LANTRONIX | EMG8500 |
Affected:
0 , < 9.7.0.1
(custom)
|
guessed | |
| LANTRONIX | EMG7500 |
Affected:
0 , < 9.7.0.1
(custom)
|
guessed | |
| LANTRONIX | SLB882 |
Affected:
*
(custom)
|
guessed | |
| LANTRONIX | SLCx-03 |
Affected:
*
(custom)
|
guessed | |
| LANTRONIX | SLCx-02 |
Affected:
*
(custom)
|
guessed |
Date Public
2026-09-18 00:00
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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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