CVE-2022-0020 (GCVE-0-2022-0020)
Vulnerability from cvelistv5 – Published: 2022-02-10 18:10 – Updated: 2024-09-16 16:53
VLAI
EPSS
VEX
Title
Cortex XSOAR: Stored Cross-Site Scripting (XSS) Vulnerability in Web Interface
Summary
A stored cross-site scripting (XSS) vulnerability in Palo Alto Network Cortex XSOAR web interface enables an authenticated network-based attacker to store a persistent javascript payload that will perform arbitrary actions in the Cortex XSOAR web interface on behalf of authenticated administrators who encounter the payload during normal operations. This issue impacts: All builds of Cortex XSOAR 6.1.0; Cortex XSOAR 6.2.0 builds earlier than build 1958888.
Severity
6.8 (Medium)
CWE
- CWE-79 - Cross-site Scripting (XSS)
Assigner
References
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Palo Alto Networks | Cortex XSOAR |
Unaffected:
6.5.0 all
Affected: 6.1.0 all Affected: 6.2.0 , < 1958888 (custom) |
guessed |
Date Public
2022-02-09 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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