CVE-2026-31431 — “Copy Fail” Linux Kernel Vulnerability

CVE DETAILS

CVE ID: CVE-2026-31431
Name: Copy Fail
Type: Local Privilege Escalation (LPE)
Affected Component: Linux Kernel (AF_ALG – algif_aead)
CVSS Score: 7.8
Severity: High
Attack Vector: Local
Privileges Required: Low
User Interaction: None
Exploit Complexity: Low
Exploit Availability: Public PoC available (for educational purposes only)
Exploit Reliability: High
Affected Versions: Linux kernels released from around 2017 onward
Patch Status: Available

Introduction

A newly disclosed Linux kernel issue, tracked as CVE-2026-31431 and widely referred to as “Copy Fail,” has raised concerns across the security community. The reason is simple: it allows a normal user on a system to become root without needing any special permissions or complicated tricks.

What stands out here is not just the impact, but the way the attack works. Instead of modifying files directly, the change happens quietly in memory. That means many traditional security tools won’t notice anything unusual.

Because Linux systems are used everywhere—from servers and cloud platforms to developer machines—the exposure is quite broad. Systems that haven’t been updated recently may already be at risk.

Technical Background

To understand this issue, it helps to look at how the Linux kernel handles cryptographic operations. There is an interface called AF_ALG that allows applications to use encryption features directly from the kernel.

Inside this interface, a module named algif_aead is responsible for handling AEAD operations. These operations are commonly used when both encryption and integrity checks are needed.

Under normal conditions, this mechanism works safely. However, a flaw in how memory is handled during certain operations allows unintended behavior.

Instead of working only with user-controlled buffers, the kernel can end up interacting with memory that is tied to files. This memory is part of the page cache, which is used to speed up file access.

By carefully controlling how data flows through this process, it becomes possible to change file content in memory without touching the file on disk.

Root Cause

The issue comes down to how in-place operations are handled during cryptographic processing. In simple terms, the same memory area can sometimes be used for both input and output.

While this approach improves efficiency, it also introduces risk. The kernel does not always verify whether the memory being written to is safe.

Because of this:

File-backed memory can be modified indirectly
Data that should be read-only becomes writable in memory
Boundaries between trusted and untrusted data are not properly enforced

This creates an opportunity for controlled memory modification.

Exploitation Process

The attack itself is not complicated. It uses normal system functionality in a specific sequence.

A typical scenario looks like this:

A user with limited access starts by creating an AF_ALG socket. Then, the AEAD operation is configured with specific parameters. The attacker uses the splice() system call to connect file-backed memory into the process. Carefully prepared input is then passed into the system.

At this stage, a small portion of a binary file—usually one with elevated privileges—is modified in memory. The modified binary is executed before the system refreshes the cached data.

Even a small change is enough to alter how the program behaves, often allowing authentication checks to be bypassed.

Exploit Flow Overview

The diagram below explains the attack path in a simple way, showing how a low-privileged user moves through different stages to eventually gain root access.

Figure: Simplified flow of the Copy Fail exploit showing memory manipulation via AF_ALG and page cache leading to privilege escalation.

Target Binaries

The exploit usually focuses on binaries that already run with elevated permissions. These include:

/usr/bin/sudo
/bin/su
Other setuid-root programs

By modifying just a few bytes in memory, these programs can behave differently and grant access without proper authentication.

Proof of Concept (Educational Use Only)

A proof-of-concept has already been shared publicly. It is small, easy to understand, and written in Python.

What makes it notable is how simple it is:

No advanced payload is required
No memory corruption tricks are involved
Only standard system calls are used

This makes the vulnerability easier to reproduce in controlled environments.

Detection Challenges

One of the biggest problems with this vulnerability is detection.

Since no files are changed on disk:

File integrity tools do not trigger alerts
Logs may not show anything unusual
The system continues to run normally

Everything happens within expected system behavior, making it difficult to distinguish between legitimate and malicious activity.

Detection Strategy

Behavioral Indicators

Instead of looking for known signatures, it is more useful to watch for unusual patterns:

Non-root users interacting with AF_ALG sockets
Unexpected use of cryptographic operations
Frequent use of splice() where it is not normally expected
Privileged binaries running without proper authentication logs

Log Sources

To improve visibility, the following logs should be monitored:

auditd logs for system calls
Endpoint detection tools for behavior tracking
System logs for process execution
Kernel logs for module-level activity

Detection Rules and Queries

auditd Rules

-a always,exit -F arch=b64 -S splice -k splice_monitor
-a always,exit -F arch=b64 -S socket -F a0=38 -k af_alg_monitor

Splunk Query

index=linux_audit (syscall=splice OR syscall=socket)
| stats count by user, syscall, process
| where user!="root"

ELK / KQL Query

event.dataset: "auditd" and (process.syscall: "splice" or process.syscall: "socket") and user.name != "root"

EDR Detection Logic

A sequence-based approach works best here:

AF_ALG socket creation
Followed by splice() activity
Followed by execution of a privileged binary

This pattern is uncommon and worth investigating.

Payload Characteristics

This exploit behaves differently from traditional attacks:

Very small code size
No shellcode
No crashes or instability
Uses normal system calls

Because of this, it blends in with regular system activity.

Mitigation

Temporary Mitigation

The affected module can be disabled at boot:

initcall_blacklist=algif_aead_init

Permanent Fix

The recommended solution is to update the kernel.

Official patch:
https://git.kernel.org/linus/a664bf3d603dc3bdcf9ae47cc21e0daec706d7a5

A reboot is required after applying the update.

Affected Systems

This issue affects a wide range of systems, including:

Linux servers
Cloud environments
Virtual machines
Developer systems

Any system running kernels from around 2017 onward should be reviewed.

Risk Summary

Although the official score is listed as High, the real-world impact is more serious.

The reasons are clear:

The exploit is easy to use
It works reliably
It is difficult to detect
Many systems are affected

What This Means for Defenders

This case highlights a shift in how vulnerabilities are being exploited. Instead of relying on noisy techniques, attackers are increasingly using subtle methods that stay within normal system behavior.

That makes visibility more important than ever. Security teams need to move beyond simple alerts and start looking at patterns and context. A single system call may not mean much on its own, but a sequence of events can tell a different story.

It also shows the importance of keeping systems updated. Even a well-designed system like Linux can have hidden issues that only become visible over time.

For organizations, this is a reminder that security is not just about prevention, but also about observation. Having the right logs, the right tools, and the ability to connect events can make a big difference.

Conclusion

CVE-2026-31431 is a strong example of how a small flaw in kernel logic can lead to serious consequences. Its ability to operate silently and reliably makes it particularly concerning.

Updating affected systems should be treated as a priority. At the same time, improving monitoring and detection strategies will help reduce risk in the long term.