CVE-2026-102995
7.5 HIGHpublic exploit availablePublished 2026-09-30 · Updated 2026-10-05
AI risk analysis
- Summary
- The pypdf library is vulnerable to a memory exhaustion attack through crafted PDF files that contain oversized font mapping tokens in the /ToUnicode table. This allows attackers to cause excessive memory consumption during text extraction operations, potentially leading to denial of service.
- Exploitability
- Exploitation is relatively straightforward as it requires only a specially crafted PDF file with oversized tokens in font mapping tables, with no user interaction or authentication required.
- Blast radius
- The impact could be significant across any system using pypdf for PDF text extraction, particularly web applications processing untrusted PDF uploads, potentially causing widespread service disruption.
- Detection
- No reliable host or network indicator is derivable from the published description.
- Prioritized remediation
- Upgrade to pypdf version 6.18.1 or later to address this memory exhaustion vulnerability.
Analysis generated locally by garnet-coder (no data left the box). AI-assisted — verify against primary sources before acting.
NVD description
pypdf is a free and open-source pure-python PDF library. Prior to 6.18.1, a crafted PDF can place unusually large source-code or destination-string tokens in a font /ToUnicode mapping, causing pypdf/_cmap.py parse_bfchar to decode and retain oversized values during operations such as text extraction and consume excessive memory. This is a second follow-up to earlier /ToUnicode resource-consumption fixes and is limited to the remaining token-length path. This issue is fixed in version 6.18.1.
CVSS vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
Weaknesses
CWE-400
Vendors
pypdf project
Products
pypdf
Public exploit & PoC references
- https://github.com/py-pdf/pypdf/commit/319d0b823ce2c311a2435e9fd93c13994d32bb51[Patch]
- https://github.com/py-pdf/pypdf/pull/4071[Patch, Issue Tracking]
- https://github.com/py-pdf/pypdf/releases/tag/6.18.1[Release Notes]
- https://github.com/py-pdf/pypdf/security/advisories/GHSA-fp3h-c4fm-7vvf[Vendor Advisory]
All references
Source data: NVD (nvd.nist.gov), public domain. Exploit-DB.ai adds local AI analysis for defensive use only.
Related CVEs
- HIGHCVE-2026-102996PoC
- HIGHCVE-2026-103000PoC
- HIGHCVE-2026-102993PoC
- HIGHCVE-2026-102994PoC
- HIGHCVE-2026-102997PoC
- HIGHCVE-2026-68495PoC
- HIGHCVE-2026-68496PoC
- HIGHCVE-2026-103471PoC
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