GHSA-7652-R6CV-CV5F
Vulnerability from github – Published: 2026-03-18 18:31 – Updated: 2026-05-21 21:30In the Linux kernel, the following vulnerability has been resolved:
LoongArch: Enable exception fixup for specific ADE subcode
This patch allows the LoongArch BPF JIT to handle recoverable memory access errors generated by BPF_PROBE_MEM* instructions.
When a BPF program performs memory access operations, the instructions it executes may trigger ADEM exceptions. The kernel’s built-in BPF exception table mechanism (EX_TYPE_BPF) will generate corresponding exception fixup entries in the JIT compilation phase; however, the architecture-specific trap handling function needs to proactively call the common fixup routine to achieve exception recovery.
do_ade(): fix EX_TYPE_BPF memory access exceptions for BPF programs, ensure safe execution.
Relevant test cases: illegal address access tests in module_attach and subprogs_extable of selftests/bpf.
{
"affected": [],
"aliases": [
"CVE-2025-71270"
],
"database_specific": {
"cwe_ids": [],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2026-03-18T18:16:22Z",
"severity": "MODERATE"
},
"details": "In the Linux kernel, the following vulnerability has been resolved:\n\nLoongArch: Enable exception fixup for specific ADE subcode\n\nThis patch allows the LoongArch BPF JIT to handle recoverable memory\naccess errors generated by BPF_PROBE_MEM* instructions.\n\nWhen a BPF program performs memory access operations, the instructions\nit executes may trigger ADEM exceptions. The kernel\u2019s built-in BPF\nexception table mechanism (EX_TYPE_BPF) will generate corresponding\nexception fixup entries in the JIT compilation phase; however, the\narchitecture-specific trap handling function needs to proactively call\nthe common fixup routine to achieve exception recovery.\n\ndo_ade(): fix EX_TYPE_BPF memory access exceptions for BPF programs,\nensure safe execution.\n\nRelevant test cases: illegal address access tests in module_attach and\nsubprogs_extable of selftests/bpf.",
"id": "GHSA-7652-r6cv-cv5f",
"modified": "2026-05-21T21:30:28Z",
"published": "2026-03-18T18:31:17Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2025-71270"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/73ede654d9daa2ee41bdd17bc62946fc5a0258cb"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/9bdc1ab5e4ce6f066119018d8f69631a46f9c5a0"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/b9d9a221bd14ed4b01d113701976fa376762c544"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/c2ed4f71e9288f21d5c53ff790270758e60fa5f9"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/c49a28068363f3dca439aa5fe4d3b1f8159809fe"
}
],
"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.