CVE-2026-48155: CWE-400: Uncontrolled Resource Consumption in py-pdf pypdf
pypdf is a free and open-source pure-python PDF library. Prior to 6.12.0, an attacker who uses this vulnerability can craft a PDF which leads to large memory usage. This requires extracting text in layout mode with large character offsets. This vulnerability is fixed in 6.12.0.
AI Analysis
Technical Summary
The pypdf library versions before 6.12.0 contain a vulnerability classified as CWE-400 (Uncontrolled Resource Consumption). An attacker can create a specially crafted PDF that triggers large memory allocation during text extraction in layout mode with large character offsets. This can degrade system performance or cause denial of service due to memory exhaustion. The issue was resolved in pypdf version 6.12.0.
Potential Impact
Exploitation of this vulnerability can cause excessive memory usage on systems processing malicious PDFs with pypdf versions prior to 6.12.0. This may lead to performance degradation or denial of service conditions. There is no indication of privilege escalation, code execution, or data disclosure from the provided information.
Mitigation Recommendations
Upgrade to pypdf version 6.12.0 or later, where this vulnerability has been fixed. Patch status is confirmed by the vendor advisory stating the issue is resolved in 6.12.0. No other mitigation is indicated.
CVE-2026-48155: CWE-400: Uncontrolled Resource Consumption in py-pdf pypdf
Description
pypdf is a free and open-source pure-python PDF library. Prior to 6.12.0, an attacker who uses this vulnerability can craft a PDF which leads to large memory usage. This requires extracting text in layout mode with large character offsets. This vulnerability is fixed in 6.12.0.
CVSS v4.0
Score 4.8medium
Weaknesses
AI-Powered Analysis
Machine-generated threat intelligence
Technical Analysis
The pypdf library versions before 6.12.0 contain a vulnerability classified as CWE-400 (Uncontrolled Resource Consumption). An attacker can create a specially crafted PDF that triggers large memory allocation during text extraction in layout mode with large character offsets. This can degrade system performance or cause denial of service due to memory exhaustion. The issue was resolved in pypdf version 6.12.0.
Potential Impact
Exploitation of this vulnerability can cause excessive memory usage on systems processing malicious PDFs with pypdf versions prior to 6.12.0. This may lead to performance degradation or denial of service conditions. There is no indication of privilege escalation, code execution, or data disclosure from the provided information.
Mitigation Recommendations
Upgrade to pypdf version 6.12.0 or later, where this vulnerability has been fixed. Patch status is confirmed by the vendor advisory stating the issue is resolved in 6.12.0. No other mitigation is indicated.
Technical Details
- Data Version
- 5.2
- Assigner Short Name
- GitHub_M
- Date Reserved
- 2026-05-20T23:12:43.031Z
- Cvss Version
- 4.0
- State
- PUBLISHED
- Remediation Level
- null
Threat ID: 6a186056e29bf47b500b42c8
Added to database: 5/28/2026, 3:33:42 PM
Last enriched: 5/28/2026, 3:50:24 PM
Last updated: 5/29/2026, 7:37:11 AM
Views: 5
Community Reviews
0 reviewsCrowdsource mitigation strategies, share intel context, and vote on the most helpful responses. Sign in to add your voice and help keep defenders ahead.
Want to contribute mitigation steps or threat intel context? Sign in or create an account to join the community discussion.
Actions
Updates to AI analysis require Pro Console access. Upgrade inside Console → Billing.
Need more coverage?
Upgrade to Pro Console for AI refresh and higher limits.
For incident response and remediation, OffSeq services can help resolve threats faster.
Latest Threats
Check if your credentials are on the dark web
Instant breach scanning across billions of leaked records. Free tier available.