CVE-2023-31342 (GCVE-0-2023-31342)
Vulnerability from cvelistv5 – Published: 2025-02-11 22:24 – Updated: 2026-02-26 19:08
VLAI
EPSS
VEX
Summary
Improper input validation in the SMM handler may allow a privileged attacker to overwrite SMRAM, potentially leading to arbitrary code execution.
Severity
7.5 (High)
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-04-24 03:55 UTC
CWE
- CWE-1220 - Insufficient Granularity of Access Control
Assigner
References
Impacted products
28 products
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\"Embedded-PI FP7r2 1.0.0.9\"}], \"defaultStatus\": \"affected\"}], \"references\": [{\"url\": \"https://www.amd.com/en/resources/product-security/bulletin/amd-sb-5004.html\"}, {\"url\": \"https://www.amd.com/en/resources/product-security/bulletin/amd-sb-4008.html\"}, {\"url\": \"https://www.amd.com/en/resources/product-security/bulletin/amd-sb-3009.html\"}], \"x_generator\": {\"engine\": \"Vulnogram 0.2.0\"}, \"descriptions\": [{\"lang\": \"en\", \"value\": \"Improper input validation in the SMM handler may allow a privileged attacker to overwrite SMRAM, potentially leading to arbitrary code execution.\", \"supportingMedia\": [{\"type\": \"text/html\", \"value\": \"Improper input validation in the SMM handler may allow a privileged attacker to overwrite SMRAM, potentially leading to arbitrary code execution.\", \"base64\": false}]}], \"problemTypes\": [{\"descriptions\": [{\"lang\": \"en\", \"type\": \"CWE\", \"cweId\": \"CWE-1220\", \"description\": \"CWE-1220 Insufficient Granularity of Access Control\"}]}], \"providerMetadata\": {\"orgId\": \"b58fc414-a1e4-4f92-9d70-1add41838648\", \"shortName\": \"AMD\", \"dateUpdated\": \"2025-09-23T21:23:17.849Z\"}}}",
"cveMetadata": "{\"cveId\": \"CVE-2023-31342\", \"state\": \"PUBLISHED\", \"dateUpdated\": \"2026-02-26T19:08:51.034Z\", \"dateReserved\": \"2023-04-27T15:25:41.425Z\", \"assignerOrgId\": \"b58fc414-a1e4-4f92-9d70-1add41838648\", \"datePublished\": \"2025-02-11T22:24:02.153Z\", \"assignerShortName\": \"AMD\"}",
"dataType": "CVE_RECORD",
"dataVersion": "5.2"
}
}
}
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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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