GHSA-C6M8-29QV-7WQ9
Vulnerability from github – Published: 2024-08-17 12:30 – Updated: 2025-11-03 21:31In the Linux kernel, the following vulnerability has been resolved:
bpf, arm64: Fix trampoline for BPF_TRAMP_F_CALL_ORIG
When BPF_TRAMP_F_CALL_ORIG is set, the trampoline calls __bpf_tramp_enter() and __bpf_tramp_exit() functions, passing them the struct bpf_tramp_image *im pointer as an argument in R0.
The trampoline generation code uses emit_addr_mov_i64() to emit instructions for moving the bpf_tramp_image address into R0, but emit_addr_mov_i64() assumes the address to be in the vmalloc() space and uses only 48 bits. Because bpf_tramp_image is allocated using kzalloc(), its address can use more than 48-bits, in this case the trampoline will pass an invalid address to __bpf_tramp_enter/exit() causing a kernel crash.
Fix this by using emit_a64_mov_i64() in place of emit_addr_mov_i64() as it can work with addresses that are greater than 48-bits.
{
"affected": [],
"aliases": [
"CVE-2024-43840"
],
"database_specific": {
"cwe_ids": [],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2024-08-17T10:15:09Z",
"severity": "MODERATE"
},
"details": "In the Linux kernel, the following vulnerability has been resolved:\n\nbpf, arm64: Fix trampoline for BPF_TRAMP_F_CALL_ORIG\n\nWhen BPF_TRAMP_F_CALL_ORIG is set, the trampoline calls\n__bpf_tramp_enter() and __bpf_tramp_exit() functions, passing them\nthe struct bpf_tramp_image *im pointer as an argument in R0.\n\nThe trampoline generation code uses emit_addr_mov_i64() to emit\ninstructions for moving the bpf_tramp_image address into R0, but\nemit_addr_mov_i64() assumes the address to be in the vmalloc() space\nand uses only 48 bits. Because bpf_tramp_image is allocated using\nkzalloc(), its address can use more than 48-bits, in this case the\ntrampoline will pass an invalid address to __bpf_tramp_enter/exit()\ncausing a kernel crash.\n\nFix this by using emit_a64_mov_i64() in place of emit_addr_mov_i64()\nas it can work with addresses that are greater than 48-bits.",
"id": "GHSA-c6m8-29qv-7wq9",
"modified": "2025-11-03T21:31:11Z",
"published": "2024-08-17T12:30:32Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2024-43840"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/077149478497b2f00ff4fd9da2c892defa6418d8"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/19d3c179a37730caf600a97fed3794feac2b197b"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/6d218fcc707d6b2c3616b6cd24b948fd4825cfec"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/d9664e6ff040798a46cdc5d401064f55b8676c83"
},
{
"type": "WEB",
"url": "https://lists.debian.org/debian-lts-announce/2025/08/msg00010.html"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
}
]
}
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.
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.