theori-io/copy-fail-cve-2026-31431

Copy Fail (CVE-2026-31431): 9-year-old Linux kernel LPE found by Theori's Xint Code

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Summary Information

Updated 21 minutes ago
Added to GitGenius on September 17th, 2026
Created on April 29th, 2026
Open Issues & Pull Requests: 121 (+0)
GitHub issues: Enabled
Number of forks: 915
Total Stargazers: 4,072 (+0)
Total Subscribers: 34 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 2.7 hours
Mean response time: 13.1 hours
90th percentile: 23.6 hours
Tracked items: 85

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How this project is maintained

Roughly one issue in four opened in the past year never receives a reply. 100% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. 95% of tracked open issues have had no activity in three months, so the open count overstates what is actively being worked. Only 13% of issues opened in the past year have been closed.

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Issue Activity (beta)

Open issues: 103
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 137 days
Stale 30+ days: 102
Stale 90+ days: 97

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

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Detailed Description

Copy Fail is a Linux kernel privilege escalation exploit demonstrating a vulnerability identified as CVE-2026-31431.

The exploit addresses a local privilege escalation flaw in the Linux kernel that had remained undetected for nine years before discovery. The vulnerability was identified through automated code analysis by Xint Code, Theori's AI-driven security research tool. The repository contains the working exploit code that demonstrates how the flaw can be leveraged to gain elevated privileges on affected systems.

This repository is primarily of interest to security researchers, kernel developers, and system administrators responsible for patching and securing Linux distributions. It serves as a case study in how automated vulnerability discovery can surface long-standing kernel issues that manual review may have missed. The exploit is relevant for anyone evaluating the security posture of Linux systems or studying the effectiveness of AI-assisted security analysis in finding real-world vulnerabilities.

The project shows active security research output with focused development around a specific, reproducible vulnerability. The repository documents a concrete finding from an automated analysis pipeline, indicating ongoing work in applying machine learning techniques to kernel security analysis.