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🐍 PyPI

GHSA-ggpf-24jw-3fcw

CRITICAL

CVE-2025-24357 Malicious model remote code execution fix bypass with PyTorch < 2.6.0

Published
Apr 23, 2025
Updated
Feb 4, 2026
Affected
1 pkg
Patched
1 / 1
Exploits
None indexed

Blast Radius

1 pkg affected
🐍vllm

Real-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.

Description

Description

https://github.com/vllm-project/vllm/security/advisories/GHSA-rh4j-5rhw-hr54 reported a vulnerability where loading a malicious model could result in code execution on the vllm host. The fix applied to specify weights_only=True to calls to torch.load() did not solve the problem prior to PyTorch 2.6.0.

PyTorch has issued a new CVE about this problem: https://github.com/advisories/GHSA-53q9-r3pm-6pq6

This means that versions of vLLM using PyTorch before 2.6.0 are vulnerable to this problem.

Background Knowledge

When users install VLLM according to the official manual image

But the version of PyTorch is specified in the requirements. txt file image

So by default when the user install VLLM, it will install the PyTorch with version 2.5.1 image

In CVE-2025-24357, weights_only=True was used for patching, but we know this is not secure. Because we found that using Weights_only=True in pyTorch before 2.5.1 was unsafe

Here, we use this interface to prove that it is not safe. image

Fix

update PyTorch version to 2.6.0

Credit

This vulnerability was found By Ji'an Zhou and Li'shuo Song

Affected Packages

1 total 1 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPIvllmall versions0.8.0

Detection & mitigation playbook

Open-source dependency
  1. Detect

    Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for vllm. O3's reachability analysis confirms whether the vulnerable code path is actually invoked in your application, so you act on real exposure instead of every transitive match.

  2. Fix

    Update vllm to 0.8.0 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-ggpf-24jw-3fcw is resolved across your whole dependency graph.

  3. Workarounds

    If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.

  4. How O3 protects you

    O3 pinpoints whether GHSA-ggpf-24jw-3fcw is reachable in your code and exactly where to fix it, then blocks exploitation in production at runtime until the patched version is deployed.

Tailored to GHSA-ggpf-24jw-3fcw. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.

Frequently Asked Questions

## Description https://github.com/vllm-project/vllm/security/advisories/GHSA-rh4j-5rhw-hr54 reported a vulnerability where loading a malicious model could result in code execution on the vllm host. The fix applied to specify `weights_only=True` to calls to `torch.load()` did not solve the problem prior to PyTorch 2.6.0. PyTorch has issued a new CVE about this problem: https://github.com/advisories/GHSA-53q9-r3pm-6pq6 This means that versions of vLLM using PyTorch before 2.6.0 are vulnerable to this problem. ## Background Knowledge When users install VLLM according to the official manual ![i
O3 Security · Impact-Aware SCA

Is GHSA-ggpf-24jw-3fcw in your dependencies?

O3 detects GHSA-ggpf-24jw-3fcw across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.