Vulnerability in pytorch (UBUNTU-CVE-2026-65918)

Summary

Vulnerability in pytorch (UBUNTU-CVE-2026-65918). Risk of unauthorized operations or information disclosure.

Original description

The description as published by the source.

PyTorch torchvision through 0.28.0, fixed in commit 4e05dc2, contains an out-of-bounds heap read vulnerability in the GIF decoder's read_from_tensor callback that passes unclamped length to memcpy. Attackers can supply malicious or truncated GIF files to cause denial of service via segmentation fault or disclose adjacent heap memory contents.

Source: OSV.dev (Open Source Vulnerabilities) OSV

Auto-generated summary

Assembled by SecPulse from the source data (CVSS, CWE, affected versions) using templates — no AI involved. Check the original description and references below for exact details.

A vulnerability tracked as UBUNTU-CVE-2026-65918 has been found in pytorch.
Risk of unauthorized operations or information disclosure.
What to do: apply the vendor's official patched release.
If unsure, ask your IT team or search "pytorch UBUNTU-CVE-2026-65918" on the vendor's site.

UBUNTU-CVE-2026-65918 (pytorch)
Attack vector: remote (network-reachable) / unauthenticated
Plan: 1) Audit SBOM/dependencies, 2) Stage→prod upgrade, 3) Add WAF/proxy monitoring on affected endpoints, 4) Hunt IOCs in logs.
Refs: see the GHSA / vendor advisory / patched release linked on this page.

❓ What is the problem

A vulnerability (unclassified) exists in pytorch.

📍 Affected scope

Check the vendor advisory for the affected versions of pytorch.

🔥 Severity

Severity: ?. Risk of unauthorized operations or information disclosure.

🔧 How to fix

Update to the patched release per vendor advisory.

🛡️ Workaround

Until the patch is applied: disable the affected feature, apply WAF rules, or restrict access via network ACLs.

🔍 Detection

Search webserver/proxy logs for unusual request patterns matching this CVE's known IOCs. Run grep -r 'pytorch' . against your dependency files (package-lock.json, requirements.txt, go.sum) to find affected services.

Response actions

Concrete steps and command examples for SOC/SRE teams to execute in order

1
Identify exposure identify
```
grep -r 'pytorch' . | grep -v node_modules
```
リポジトリと本番環境の依存ファイル (package-lock.json / requirements.txt / go.sum / Gemfile.lock 等) で pytorch を grep し、稼働しているサービス・バージョンを把握する。
2
Post-deployment verification verify
```
Confirm patched version is live in production
```
パッチ適用後、ステージングで PoC または同等の悪用パターンを再現して脆弱性が閉じたことを確認。本番ではアクセスログ・WAF でアラート再発が無いか継続監視。