CVE-2023-53394 (GCVE-0-2023-53394)
Vulnerability from cvelistv5 – Published: 2025-09-18 13:33 – Updated: 2026-08-05 09:13
VLAI
EPSS
VEX
Title
net/mlx5e: xsk: Fix crash on regular rq reactivation
Summary
In the Linux kernel, the following vulnerability has been resolved:
net/mlx5e: xsk: Fix crash on regular rq reactivation
When the regular rq is reactivated after the XSK socket is closed
it could be reading stale cqes which eventually corrupts the rq.
This leads to no more traffic being received on the regular rq and a
crash on the next close or deactivation of the rq.
Kal Cuttler Conely reported this issue as a crash on the release
path when the xdpsock sample program is stopped (killed) and restarted
in sequence while traffic is running.
This patch flushes all cqes when during the rq flush. The cqe flushing
is done in the reset state of the rq. mlx5e_rq_to_ready code is moved
into the flush function to allow for this.
Severity
7.8 (High)
5.5 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-01-14 18:59 UTC
Assigner
References
Impacted products
2 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Linux | Linux |
Affected:
082a9edf12fef88400172e7d1b131d65a3ed492e , < 02a84eb2af6bea7871cd34264fb27f141f005fd9
(git)
Affected: 082a9edf12fef88400172e7d1b131d65a3ed492e , < 39646d9bcd1a65d2396328026626859a1dab59d7 (git) |
guessed | |
| Linux | Linux |
Affected:
6.1
Unaffected: 0 , < 6.1 (semver) Unaffected: 6.4.10 , ≤ 6.4.* (semver) Unaffected: 6.5 , ≤ * (original_commit_for_fix) |
guessed |
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"title": "net/mlx5e: xsk: Fix crash on regular rq reactivation",
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"engine": "bippy-1.2.0"
}
}
},
"cveMetadata": {
"assignerOrgId": "416baaa9-dc9f-4396-8d5f-8c081fb06d67",
"assignerShortName": "Linux",
"cveId": "CVE-2023-53394",
"datePublished": "2025-09-18T13:33:35.895Z",
"dateReserved": "2025-09-17T14:54:09.738Z",
"dateUpdated": "2026-05-11T19:44:07.297Z",
"state": "PUBLISHED"
},
"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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