GSD-2022-21685
Vulnerability from gsd - Updated: 2023-12-13 01:19Details
Frontier is Substrate's Ethereum compatibility layer. Prior to commit number `8a93fdc6c9f4eb1d2f2a11b7ff1d12d70bf5a664`, a bug in Frontier's MODEXP precompile implementation can cause an integer underflow in certain conditions. This will cause a node crash for debug builds. For release builds (and production WebAssembly binaries), the impact is limited as it can only cause a normal EVM out-of-gas. Users who do not use MODEXP precompile in their runtime are not impacted. A patch is available in pull request #549.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2022-21685",
"description": "Frontier is Substrate\u0027s Ethereum compatibility layer. Prior to commit number `8a93fdc6c9f4eb1d2f2a11b7ff1d12d70bf5a664`, a bug in Frontier\u0027s MODEXP precompile implementation can cause an integer underflow in certain conditions. This will cause a node crash for debug builds. For release builds (and production WebAssembly binaries), the impact is limited as it can only cause a normal EVM out-of-gas. Users who do not use MODEXP precompile in their runtime are not impacted. A patch is available in pull request #549.",
"id": "GSD-2022-21685"
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
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],
"details": "Frontier is Substrate\u0027s Ethereum compatibility layer. Prior to commit number `8a93fdc6c9f4eb1d2f2a11b7ff1d12d70bf5a664`, a bug in Frontier\u0027s MODEXP precompile implementation can cause an integer underflow in certain conditions. This will cause a node crash for debug builds. For release builds (and production WebAssembly binaries), the impact is limited as it can only cause a normal EVM out-of-gas. Users who do not use MODEXP precompile in their runtime are not impacted. A patch is available in pull request #549.",
"id": "GSD-2022-21685",
"modified": "2023-12-13T01:19:14.826887Z",
"schema_version": "1.4.0"
}
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},
"lastModifiedDate": "2022-01-21T20:40Z",
"publishedDate": "2022-01-14T17:15Z"
}
}
}
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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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