Severity by source
CVSS:4.0/AV:L/AC:L/AT:N/PR:N/UI:P/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X
AV:N/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:H
Primary rating from GitHub Advisory.
CVSS VectorGitHub Advisory
CVSS:4.0/AV:L/AC:L/AT:N/PR:N/UI:P/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X
Lifecycle Timeline
7Blast Radius
ecosystem impact- 89 pypi packages depend on pypdf (19 direct, 70 indirect)
Ecosystem-wide dependent count for version 6.10.2.
DescriptionGitHub Advisory
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.
AnalysisAI
Memory exhaustion in pypdf prior to 6.10.2 allows local attackers to craft malicious PDF files that exhaust system RAM when processed. The vulnerability requires user interaction to open a specially crafted PDF containing a /FlateDecode stream with a /Predictor value other than 1 and large predictor parameters. Vendor-released patch available in version 6.10.2.
Technical ContextAI
pypdf is a pure-Python PDF library that processes PDF streams, including those using FlateDecode compression with predictor functions. The vulnerability lies in improper handling of /Predictor parameters in compressed streams. When a stream uses /FlateDecode compression with a /Predictor unequal to 1 and large predictor parameters, the decompression and predictor application logic fails to validate or limit memory allocation, leading to CWE-789 (Memory Allocation with Excessive Size Value). The predictor function in PDF streams is used to filter data during compression; improper parameter validation allows an attacker to specify predictor parameters that cause the library to allocate excessive memory during decompression.
RemediationAI
Upgrade pypdf to version 6.10.2 or later immediately. For users unable to upgrade immediately, apply the patch changes manually from commit ac734dab4eef92bcce50d503949b4d9887d89f11 (https://github.com/py-pdf/pypdf/commit/ac734dab4eef92bcce50d503949b4d9887d89f11), which implements proper validation of predictor parameters. The patch validates that predictor parameters do not cause excessive memory allocation during stream decompression. As an interim compensating control, restrict access to PDF processing to trusted sources only and implement file size limits on uploaded or processed PDFs to reduce the likelihood of triggering the memory exhaustion. Monitor system memory usage during PDF processing and set resource limits (ulimit on Linux) to prevent complete system resource exhaustion if a malicious PDF is processed. Note that these controls reduce risk but do not eliminate the vulnerability - patching remains the primary remediation.
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Same technique Information Disclosure
View allVendor StatusVendor
SUSE
Severity: MediumShare
External POC / Exploit Code
Leaving vuln.today
EUVD-2026-25112