GHSA-9MVC-8737-8J8H
Vulnerability from github – Published: 2026-02-18 22:41 – Updated: 2026-02-23 22:21
VLAI
Summary
pypdf possibly has long runtimes for malformed FlateDecode streams
Details
Impact
An attacker who uses this vulnerability can craft a PDF which leads to long runtimes. This requires a malformed /FlateDecode stream, where the byte-by-byte decompression is used.
Patches
This has been fixed in pypdf==6.7.1.
Workarounds
If you cannot upgrade yet, consider applying the changes from PR #3644.
Severity
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "pypdf"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "6.7.1"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2026-27026"
],
"database_specific": {
"cwe_ids": [
"CWE-770"
],
"github_reviewed": true,
"github_reviewed_at": "2026-02-18T22:41:24Z",
"nvd_published_at": "2026-02-20T22:16:29Z",
"severity": "MODERATE"
},
"details": "### Impact\n\nAn attacker who uses this vulnerability can craft a PDF which leads to long runtimes. This requires a malformed `/FlateDecode` stream, where the byte-by-byte decompression is used.\n\n### Patches\n\nThis has been fixed in [pypdf==6.7.1](https://github.com/py-pdf/pypdf/releases/tag/6.7.1).\n\n### Workarounds\n\nIf you cannot upgrade yet, consider applying the changes from PR [#3644](https://github.com/py-pdf/pypdf/pull/3644).",
"id": "GHSA-9mvc-8737-8j8h",
"modified": "2026-02-23T22:21:03Z",
"published": "2026-02-18T22:41:24Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/py-pdf/pypdf/security/advisories/GHSA-9mvc-8737-8j8h"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-27026"
},
{
"type": "WEB",
"url": "https://github.com/py-pdf/pypdf/pull/3644"
},
{
"type": "WEB",
"url": "https://github.com/py-pdf/pypdf/commit/7905842d833f899f1d3228af7e7467ad80277016"
},
{
"type": "PACKAGE",
"url": "https://github.com/py-pdf/pypdf"
},
{
"type": "WEB",
"url": "https://github.com/py-pdf/pypdf/releases/tag/6.7.1"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:4.0/AV:L/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N",
"type": "CVSS_V4"
}
],
"summary": "pypdf possibly has long runtimes for malformed FlateDecode streams"
}
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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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