CVE-2026-9504 (GCVE-0-2026-9504)
Vulnerability from cvelistv5 – Published: 2026-05-25 21:15 – Updated: 2026-05-26 15:11 X_Open Source
VLAI
EPSS
VEX
Title
GNU LibreDWG Dwggrep Utility dwggrep.c bit_convert_TU out-of-bounds
Summary
A weakness has been identified in GNU LibreDWG up to 0.14. Affected is the function bit_convert_TU of the file programs/dwggrep.c of the component Dwggrep Utility. This manipulation causes out-of-bounds read. The attack needs to be launched locally. The exploit has been made available to the public and could be used for attacks. Patch name: be996bf2178a40e98720f18c2414815d244413db. Applying a patch is the recommended action to fix this issue.
Severity
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-05-26 15:10 UTC
Assigner
References
7 references
| URL | Tags |
|---|---|
| https://vuldb.com/vuln/365486 | vdb-entrytechnical-description |
| https://vuldb.com/vuln/365486/cti | signaturepermissions-required |
| https://vuldb.com/submit/814261 | third-party-advisory |
| https://github.com/LibreDWG/libredwg/issues/1246 | issue-tracking |
| https://github.com/HackC0der/CVE-Repos/blob/main/… | exploit |
| https://github.com/LibreDWG/libredwg/commit/be996… | patch |
| https://www.gnu.org/ | product |
Impacted products
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"url": "https://www.gnu.org/"
}
],
"tags": [
"x_open-source"
],
"timeline": [
{
"lang": "en",
"time": "2026-05-25T00:00:00.000Z",
"value": "Advisory disclosed"
},
{
"lang": "en",
"time": "2026-05-25T02:00:00.000Z",
"value": "VulDB entry created"
},
{
"lang": "en",
"time": "2026-05-25T12:10:05.000Z",
"value": "VulDB entry last update"
}
],
"title": "GNU LibreDWG Dwggrep Utility dwggrep.c bit_convert_TU out-of-bounds"
}
},
"cveMetadata": {
"assignerOrgId": "1af790b2-7ee1-4545-860a-a788eba489b5",
"assignerShortName": "VulDB",
"cveId": "CVE-2026-9504",
"datePublished": "2026-05-25T21:15:11.611Z",
"dateReserved": "2026-05-25T10:04:28.109Z",
"dateUpdated": "2026-05-26T15:11:22.474Z",
"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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