GHSA-R2JJ-8GWV-75PJ
Vulnerability from github – Published: 2026-10-15 06:30 – Updated: 2026-10-16 06:30In DragonFly BSD through 6.4.2 before 4bb22ff, the ARP input handlers arpintr() and in_arpinput() (sys/netinet/if_ether.c) do not validate the hardware address length (ar_hln) and protocol address length (ar_pln) fields of incoming ARP packets. in_arpreply() then uses these attacker-controlled values as memcpy lengths when copying from kernel buffers that are only 6 and 4 bytes long, resulting in an out-of-bounds read of kernel heap and stack memory that is copied into the ARP reply sent back to the requester. An unauthenticated attacker on the adjacent network can use a single crafted ARP request to disclose up to roughly 250 bytes of each type of kernel memory, including kernel pointers. This is expected to be fixed in the 6.6 stable release when that is available.
{
"affected": [],
"aliases": [
"CVE-2026-110912"
],
"database_specific": {
"cwe_ids": [
"CWE-130"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2026-10-15T04:18:23Z",
"severity": "HIGH"
},
"details": "In DragonFly BSD through 6.4.2 before 4bb22ff, the ARP input handlers arpintr() and in_arpinput() (sys/netinet/if_ether.c) do not validate the hardware address length (ar_hln) and protocol address length (ar_pln) fields of incoming ARP packets. in_arpreply() then uses these attacker-controlled values as memcpy lengths when copying from kernel buffers that are only 6 and 4 bytes long, resulting in an out-of-bounds read of kernel heap and stack memory that is copied into the ARP reply sent back to the requester. An unauthenticated attacker on the adjacent network can use a single crafted ARP request to disclose up to roughly 250 bytes of each type of kernel memory, including kernel pointers. This is expected to be fixed in the 6.6 stable release when that is available.",
"id": "GHSA-r2jj-8gwv-75pj",
"modified": "2026-10-16T06:30:23Z",
"published": "2026-10-15T06:30:42Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-110912"
},
{
"type": "WEB",
"url": "https://github.com/DragonFlyBSD/DragonFlyBSD/commit/4bb22ffcf2e9fb44995fbfd9945194afc87fb182"
},
{
"type": "WEB",
"url": "https://github.com/dreamfast/CVE-2026-110912"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:4.0/AV:A/AC:L/AT:N/PR:N/UI:N/VC:H/VI:N/VA:N/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X",
"type": "CVSS_V4"
}
]
}
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.
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.
Vulnerability timeline
Sightings and recorded dates, grouped by day · UTC
Loading timeline…
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.
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.