CVE-2026-83801 (GCVE-0-2026-83801)
Vulnerability from cvelistv5 – Published: 2026-09-22 19:44 – Updated: 2026-09-23 15:08
VLAI
EPSS
VEX
Title
Nautobot: Stored cross-site scripting (XSS) in object create/edit form help text
Summary
Nautobot is a Network Source of Truth and Network Automation Platform. Prior to 2.4.37 and 3.1.8, a user with extras.add_relationship or extras.change_relationship permission can store HTML or JavaScript in a Relationship description, and a user with dcim.add_modulefamily or dcim.change_modulefamily permission can store it in a Module Family name. Nautobot assigns these values to form field help_text rendered by render_field.html through Django's |safe filter without adequate neutralization. The stored content executes in the authenticated browser session of any user, including an administrator or superuser, who opens an affected create or edit form. This can enable actions as the victim, session or token theft, and further privilege escalation. This issue is fixed in versions 2.4.37 and 3.1.8.
Severity
5.4 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-23 15:08 UTC
CWE
- CWE-79 - Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting')
Assigner
References
5 references
| URL | Tags |
|---|---|
| https://github.com/nautobot/nautobot/security/adv… | x_refsource_CONFIRM |
| https://github.com/nautobot/nautobot/commit/e6ff2… | x_refsource_MISC |
| https://github.com/nautobot/nautobot/commit/f8a62… | x_refsource_MISC |
| https://github.com/nautobot/nautobot/releases/tag… | x_refsource_MISC |
| https://github.com/nautobot/nautobot/releases/tag… | x_refsource_MISC |
Impacted products
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"version": "2.0.3"
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"orgId": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
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"version": "\u003c 2.4.37"
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{
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"version": "\u003e= 3.0.0, \u003c 3.1.8"
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"userInteraction": "REQUIRED",
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"version": "3.1"
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},
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"tags": [
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"discovery": "UNKNOWN"
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}
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"lastModified": "2026-09-23T18:12:04.247",
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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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