GHSA-HJCQ-5876-2CPR
Vulnerability from github – Published: 2026-09-24 18:31 – Updated: 2026-09-24 18:31
VLAI
Details
nDPI 5.1.0 contains a memory access issue in the DNS dissector and serializer deserialization code. Specially crafted network input can cause byte-buffer addresses at odd offsets to be cast to uint16_t or wider integer pointers and directly dereferenced without alignment checks. This results in undefined behavior and can cause process termination in UBSan-instrumented builds or on strict-alignment architectures, leading to denial of service.
Severity
7.5 (High)
{
"affected": [],
"aliases": [
"CVE-2026-88357"
],
"database_specific": {
"cwe_ids": [
"CWE-1335"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2026-09-24T16:17:12Z",
"severity": "HIGH"
},
"details": "nDPI 5.1.0 contains a memory access issue in the DNS dissector and serializer deserialization code. Specially crafted network input can cause byte-buffer addresses at odd offsets to be cast to uint16_t or wider integer pointers and directly dereferenced without alignment checks. This results in undefined behavior and can cause process termination in UBSan-instrumented builds or on strict-alignment architectures, leading to denial of service.",
"id": "GHSA-hjcq-5876-2cpr",
"modified": "2026-09-24T18:31:19Z",
"published": "2026-09-24T18:31:19Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-88357"
},
{
"type": "WEB",
"url": "https://github.com/ntop/nDPI/issues/3213"
},
{
"type": "WEB",
"url": "https://github.com/ntop/nDPI/pull/3231"
},
{
"type": "WEB",
"url": "https://github.com/utoni/nDPI/commit/6698f14bf6025394a537fe23f413cf79e9d13594"
},
{
"type": "WEB",
"url": "https://github.com/utoni/nDPI/commit/694231bb43ff21f452fbc2b53fcf6cdc9a99f75f"
},
{
"type": "WEB",
"url": "https://github.com/utoni/nDPI/commit/6ce1280c52ed52a97332c3fe58fd2b47b867ec6e"
},
{
"type": "WEB",
"url": "https://github.com/utoni/nDPI/commit/8fc3b439920021a77165f8aee4f81b25bc88629e"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
}
]
}
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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.
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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.
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.
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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.
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