CVE-2026-22691 (GCVE-0-2026-22691)
Vulnerability from cvelistv5 – Published: 2026-01-10 04:46 – Updated: 2026-01-12 16:48
VLAI
EPSS
VEX
Title
pypdf has possible long runtimes for malformed startxref
Summary
pypdf is a free and open-source pure-python PDF library. Prior to version 6.6.0, pypdf has possible long runtimes for malformed startxref. An attacker who uses this vulnerability can craft a PDF which leads to possibly long runtimes for invalid startxref entries. When rebuilding the cross-reference table, PDF files with lots of whitespace characters become problematic. Only the non-strict reading mode is affected. Only the non-strict reading mode is affected. This issue has been patched in version 6.6.0.
Severity
SSVC
Exploitation: none
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-01-12 16:48 UTC
CWE
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://github.com/py-pdf/pypdf/security/advisori… | x_refsource_CONFIRM |
| https://github.com/py-pdf/pypdf/pull/3594 | x_refsource_MISC |
| https://github.com/py-pdf/pypdf/commit/294165726b… | x_refsource_MISC |
| https://github.com/py-pdf/pypdf/releases/tag/6.6.0 | x_refsource_MISC |
Impacted products
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"lang": "es",
"value": "pypdf es una biblioteca PDF de Python puro, gratuita y de c\u00f3digo abierto. Antes de la versi\u00f3n 6.6.0, pypdf presenta posibles tiempos de ejecuci\u00f3n prolongados para \u0027startxref\u0027 malformados. Un atacante que utiliza esta vulnerabilidad puede crear un PDF que provoca posibles tiempos de ejecuci\u00f3n prolongados para entradas \u0027startxref\u0027 no v\u00e1lidas. Al reconstruir la tabla de referencias cruzadas, los archivos PDF con muchos caracteres de espacio en blanco se vuelven problem\u00e1ticos. Solo el modo de lectura no estricto se ve afectado. Solo el modo de lectura no estricto se ve afectado. Este problema ha sido parcheado en la versi\u00f3n 6.6.0."
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"id": "CVE-2026-22691",
"lastModified": "2026-06-17T10:20:14.553",
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"modifiedVulnAvailabilityImpact": "NOT_DEFINED",
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"modifiedVulnIntegrityImpact": "NOT_DEFINED",
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"valueDensity": "NOT_DEFINED",
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"version": "4.0",
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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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