CVE-2026-41312: CWE-789: Memory Allocation with Excessive Size Value in py-pdf pypdf
pypdf is a free and open-source pure-python PDF library. An attacker who uses a vulnerability present in versions prior to 6.10.2 can craft a PDF which leads to the RAM being exhausted. This requires accessing a stream compressed using `/FlateDecode` with a `/Predictor` unequal 1 and large predictor parameters. This has been fixed in pypdf 6.10.2. As a workaround, one may apply the changes from the patch manually.
AI Analysis
Technical Summary
The vulnerability in pypdf (CVE-2026-41312) arises from improper handling of PDF streams compressed with /FlateDecode when the /Predictor parameter is not equal to 1 and large predictor parameters are used. This causes the library to allocate excessive memory, potentially exhausting RAM. The flaw affects all pypdf versions before 6.10.2. The vendor fixed the issue in version 6.10.2, preventing crafted PDFs from causing memory exhaustion.
Potential Impact
An attacker can craft a malicious PDF that, when processed by vulnerable versions of pypdf, causes the application to consume excessive RAM, potentially leading to denial of service due to resource exhaustion. There is no indication of code execution or data corruption. The attack requires user interaction to open or process the malicious PDF and local access to the vulnerable library.
Mitigation Recommendations
A fix is available in pypdf version 6.10.2. Users should upgrade to this version to fully remediate the vulnerability. Alternatively, users may manually apply the patch changes if upgrading is not immediately possible. No other vendor advisories or cloud service mitigations are indicated.
CVE-2026-41312: CWE-789: Memory Allocation with Excessive Size Value in py-pdf pypdf
Description
pypdf is a free and open-source pure-python PDF library. An attacker who uses a vulnerability present in versions prior to 6.10.2 can craft a PDF which leads to the RAM being exhausted. This requires accessing a stream compressed using `/FlateDecode` with a `/Predictor` unequal 1 and large predictor parameters. This has been fixed in pypdf 6.10.2. As a workaround, one may apply the changes from the patch manually.
AI-Powered Analysis
Machine-generated threat intelligence
Technical Analysis
The vulnerability in pypdf (CVE-2026-41312) arises from improper handling of PDF streams compressed with /FlateDecode when the /Predictor parameter is not equal to 1 and large predictor parameters are used. This causes the library to allocate excessive memory, potentially exhausting RAM. The flaw affects all pypdf versions before 6.10.2. The vendor fixed the issue in version 6.10.2, preventing crafted PDFs from causing memory exhaustion.
Potential Impact
An attacker can craft a malicious PDF that, when processed by vulnerable versions of pypdf, causes the application to consume excessive RAM, potentially leading to denial of service due to resource exhaustion. There is no indication of code execution or data corruption. The attack requires user interaction to open or process the malicious PDF and local access to the vulnerable library.
Mitigation Recommendations
A fix is available in pypdf version 6.10.2. Users should upgrade to this version to fully remediate the vulnerability. Alternatively, users may manually apply the patch changes if upgrading is not immediately possible. No other vendor advisories or cloud service mitigations are indicated.
Technical Details
- Data Version
- 5.2
- Assigner Short Name
- GitHub_M
- Date Reserved
- 2026-04-20T14:01:46.671Z
- Cvss Version
- 4.0
- State
- PUBLISHED
- Remediation Level
- null
Threat ID: 69e93e1919fe3cd2cdf2afd7
Added to database: 4/22/2026, 9:31:05 PM
Last enriched: 4/22/2026, 9:46:14 PM
Last updated: 4/22/2026, 11:43:30 PM
Views: 7
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