CVE-2026-87825 (GCVE-0-2026-87825)
Vulnerability from cvelistv5 – Published: 2026-09-09 14:32 – Updated: 2026-09-14 13:35
VLAI
EPSS
VEX
Title
zstd-jni 1.3.8-4 through 1.5.7-13 Use-After-Free of Compression and Decompression Dictionaries
Summary
zstd-jni before 1.5.7-14 contains a use-after-free vulnerability where streams and contexts hold a dictionary's shared lock only during the load call, allowing the dictionary to be closed while still referenced. Attackers can close a dictionary after associating it with a stream or context, causing subsequent read or write operations to access freed native memory, resulting in silent data corruption or JVM crashes.
Severity
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-14 13:34 UTC
CWE
- CWE-416 - Use After Free
Assigner
References
9 references
| URL | Tags |
|---|---|
| https://github.com/luben/zstd-jni/security/adviso… | vendor-advisory |
| https://github.com/luben/zstd-jni/commit/393d7311… | patch |
| https://github.com/luben/zstd-jni/commit/a560131d… | patch |
| https://github.com/luben/zstd-jni/blob/v1.5.7-13/… | technical-description |
| https://github.com/luben/zstd-jni/blob/v1.5.7-13/… | technical-description |
| https://github.com/luben/zstd-jni/releases/tag/v1… | release-notes |
| https://github.com/luben/zstd-jni/commit/0827ed02… | technical-descriptionpatch |
| https://github.com/luben/zstd-jni | product |
| https://www.vulncheck.com/advisories/zstd-jni-1.3… | third-party-advisory |
Impacted products
Date Public
2026-08-19 00:00
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"title": "zstd-jni 1.3.8-4 through 1.5.7-13 Use-After-Free of Compression and Decompression Dictionaries",
"x_generator": {
"engine": "vulncheck-endgame"
}
}
},
"cveMetadata": {
"assignerOrgId": "83251b91-4cc7-4094-a5c7-464a1b83ea10",
"assignerShortName": "VulnCheck",
"cveId": "CVE-2026-87825",
"datePublished": "2026-09-09T14:32:44.795Z",
"dateReserved": "2026-09-09T10:32:34.110Z",
"dateUpdated": "2026-09-14T13:35:21.082Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.2"
}
}
}
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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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