Containerd, Layer Unpack Handler Vulnerability, CVE-2026-95838 (medium) -DC-Oct2026-3254

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A vulnerability exists within containerd’s layer unpack handler where the processing of specifically structured image layers introduces superlinear time complexity. When an affected version of containerd unpacks a maliciously crafted container image, particular file configurations and opaque whiteout structures trigger repeated, redundant directory traversals. This algorithmic inefficiency causes a sharp spike in CPU resource consumption and heavily extends image extraction times. Consequently, operations such as pulling or importing container images take significantly longer to complete, directly delaying container startup sequences across standalone engines and orchestration platforms alike. In environments like Kubernetes that enforce strict global concurrency limits on concurrent image pulls, these resource-intensive, prolonged extraction tasks starve other unrelated container deployments of execution threads, creating a bottleneck that degrades overall cluster performance and availability.

DailyCVE Form:

Platform: containerd
Version: <1.7.35
Vulnerability : path traversal
Severity: medium
date: 2026-09-22

Prediction: 2026-09-25

What Undercode Say

ctr images pull docker.io/library/malicious-image:latest
nerdctl run --d docker.io/library/malicious-image:latest

How Exploit: (Educational Purposes!)

Conceptual demonstration of layer file configuration trigger
import tarfile
with tarfile.open("malicious_layer.tar", "w") as tar:
Crafting repeated traversals via opaque whiteouts / configurations
for i in range(10000):
info = tarfile.TarInfo(name=f"etc/trick/path{i}/../../../../etc/passwd")
tar.addfile(info)

Protection: from this CVE

Upgrade containerd to version 1.7.35, 2.0.12, 2.2.8, 2.3.5 or later. Avoid pulling or importing container images from untrusted or unknown registries.

Impact

Maliciously crafted container images trigger superlinear time complexity during extraction, exhausting CPU resources, delaying container startups, and reducing pull concurrency in orchestrators like Kubernetes.

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Sources:

Reported By: github.com
Extra Source Hub:
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