CVE-2021-46906 (GCVE-0-2021-46906)
Vulnerability from cvelistv5 – Published: 2024-02-26 17:20 – Updated: 2026-05-11 13:44
VLAI
EPSS
VEX
Title
HID: usbhid: fix info leak in hid_submit_ctrl
Summary
In the Linux kernel, the following vulnerability has been resolved:
HID: usbhid: fix info leak in hid_submit_ctrl
In hid_submit_ctrl(), the way of calculating the report length doesn't
take into account that report->size can be zero. When running the
syzkaller reproducer, a report of size 0 causes hid_submit_ctrl) to
calculate transfer_buffer_length as 16384. When this urb is passed to
the usb core layer, KMSAN reports an info leak of 16384 bytes.
To fix this, first modify hid_report_len() to account for the zero
report size case by using DIV_ROUND_UP for the division. Then, call it
from hid_submit_ctrl().
Severity
No CVSS data available.
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-09-10 16:02 UTC
Assigner
References
8 references
Impacted products
2 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Linux | Linux |
Affected:
1da177e4c3f41524e886b7f1b8a0c1fc7321cac2 , < c5d3c142f2d57d40c55e65d5622d319125a45366
(git)
Affected: 1da177e4c3f41524e886b7f1b8a0c1fc7321cac2 , < 41b1e71a2c57366b08dcca1a28b0d45ca69429ce (git) Affected: 1da177e4c3f41524e886b7f1b8a0c1fc7321cac2 , < 8c064eece9a51856f3f275104520c7e3017fc5c0 (git) Affected: 1da177e4c3f41524e886b7f1b8a0c1fc7321cac2 , < 0e280502be1b003c3483ae03fc60dea554fcfa82 (git) Affected: 1da177e4c3f41524e886b7f1b8a0c1fc7321cac2 , < 7f5a4b24cdbd7372770a02f23e347d7d9a9ac8f1 (git) Affected: 1da177e4c3f41524e886b7f1b8a0c1fc7321cac2 , < b1e3596416d74ce95cc0b7b38472329a3818f8a9 (git) Affected: 1da177e4c3f41524e886b7f1b8a0c1fc7321cac2 , < 21883bff0fd854e07429a773ff18f1e9658f50e8 (git) Affected: 1da177e4c3f41524e886b7f1b8a0c1fc7321cac2 , < 6be388f4a35d2ce5ef7dbf635a8964a5da7f799f (git) |
guessed | |
| Linux | Linux |
Affected:
2.6.12
Unaffected: 0 , < 2.6.12 (semver) Unaffected: 4.4.274 , ≤ 4.4.* (semver) Unaffected: 4.9.274 , ≤ 4.9.* (semver) Unaffected: 4.14.238 , ≤ 4.14.* (semver) Unaffected: 4.19.196 , ≤ 4.19.* (semver) Unaffected: 5.4.127 , ≤ 5.4.* (semver) Unaffected: 5.10.45 , ≤ 5.10.* (semver) Unaffected: 5.12.12 , ≤ 5.12.* (semver) Unaffected: 5.13 , ≤ * (original_commit_for_fix) |
guessed |
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Drag nodes to arrange them. Double-click a related vulnerability to open it, or a source to visit its link. The Physics button on the left tunes the layout and the simulation; the legend at the bottom left hides or shows nodes by kind and by sighting type.
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.
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- Exploited: The vulnerability was observed as exploited 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.
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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.