CVE-2022-23822 (GCVE-0-2022-23822)
Vulnerability from cvelistv5 – Published: 2022-04-27 16:06 – Updated: 2024-09-16 22:25
VLAI
EPSS
VEX
Summary
In this physical attack, an attacker may potentially exploit the Zynq-7000 SoC First Stage Boot Loader (FSBL) by bypassing authentication and loading a malicious image onto the device. This in turn may further allow the attacker to perform additional attacks such as such as using the device as a decryption oracle. An anticipated mitigation via a 2022.1 patch will resolve the issue.
Severity
6.8 (Medium)
CWE
- CWE-863 - Incorrect Authorization
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://support.xilinx.com/s/article/76974 | x_refsource_MISC |
| https://github.com/Xilinx/embeddedsw/tree/master/… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| AMD-Xilinx | Zynq-7000 SoC FSBL |
Affected:
2021.2 , ≤ 2022.1
(custom)
|
guessed |
Date Public
2022-04-26 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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