The _load_model() function in the neural_magic_training.py script of the optimate project in...
Summary
Vulnerability reported (CVE-2026-31219). Successful exploitation can lead to full system takeover.
Original description
The description as published by the source.
The _load_model() function in the neural_magic_training.py script of the optimate project in commit a6d302f912b481c94370811af6b11402f51d377f (2024-07-21) is vulnerable to insecure deserialization (CWE-502). When a user provides a single model file path (e.g., .pt or .pth) via the --model command-line argument, the function loads the file using torch.load() without enabling the weights_only=True security parameter. This allows the deserialization of arbitrary Python objects through the Pickle module. A remote attacker can exploit this by providing a maliciously crafted model file, leading to arbitrary code execution during deserialization on the victim's system.
Source: NIST National Vulnerability Database NVD GitHub Advisory
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 CVE-2026-31219 has been found.
Successful exploitation can lead to full system takeover. CVSS score: 8.8/10.
What to do: apply the vendor's official patched release.
If unsure, ask your IT team or search "CVE-2026-31219" on the vendor's site.
CVE-2026-31219 — CWE-502 / CVSS v3 8.8
Attack vector: remote (network-reachable) / no user interaction
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.
Unsafe deserialization (CWE-502) has been reported.
Check the vendor advisory for the affected versions.
Severity: High (CVSS 8.8/10). Successful exploitation can lead to full system takeover.
Update to the patched release per vendor advisory.
Until the patch is applied: disable the affected feature, apply WAF rules, or restrict access via network ACLs.
Search webserver/proxy logs for unusual request patterns matching this CVE's known IOCs. Audit SBOM/dependencies to find affected services.
Response actions
Concrete steps and command examples for SOC/SRE teams to execute in order
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1Identify exposure identify
Audit SBOM/dependencies for affected components.資産台帳・SBOM・依存マニフェストで影響コンポーネントを特定する。
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2Post-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。
References
- web cve@mitre.org
- web cve@mitre.org
- web nvd.nist.gov
- web github.com