GHSA-WHMQ-HWCM-HPC6
Vulnerability from github – Published: 2025-05-02 18:31 – Updated: 2025-05-02 18:31
VLAI
Details
In the Linux kernel, the following vulnerability has been resolved:
net: usb: smsc75xx: Move packet length check to prevent kernel panic in skb_pull
Packet length check needs to be located after size and align_count calculation to prevent kernel panic in skb_pull() in case rx_cmd_a & RX_CMD_A_RED evaluates to true.
{
"affected": [],
"aliases": [
"CVE-2023-53104"
],
"database_specific": {
"cwe_ids": [],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2025-05-02T16:15:29Z",
"severity": null
},
"details": "In the Linux kernel, the following vulnerability has been resolved:\n\nnet: usb: smsc75xx: Move packet length check to prevent kernel panic in skb_pull\n\nPacket length check needs to be located after size and align_count\ncalculation to prevent kernel panic in skb_pull() in case\nrx_cmd_a \u0026 RX_CMD_A_RED evaluates to true.",
"id": "GHSA-whmq-hwcm-hpc6",
"modified": "2025-05-02T18:31:36Z",
"published": "2025-05-02T18:31:36Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2023-53104"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/181e9db5caec4daae57a58675a1ceaa47348958f"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/2cc46ed406bb325f10a251b03d9a83ae67b3d3d8"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/43ffe6caccc7a1bb9d7442fbab521efbf6c1378c"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/4caee8e7d91e4f06f21881726da9c1bb2cd6e4fa"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/5c96eeba93980c5cef5176a4bac0ddb722fd57dc"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/7bf0eac3fdd2d25f5c6ceab63e3e4902e274f7ee"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/89441504d66d116eb5ce58c132f58cdcca5b498a"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/e0d07a3203c36d073af2177edfc6b070220a60cb"
}
],
"schema_version": "1.4.0",
"severity": []
}
Loading…
Loading…
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.
Loading…
Loading…
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.
Loading…
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.
Loading…