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Crítica

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

  • Publicada: 23.04.2025
  • Actualizado: 11.10.2026 14:19

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

Software afectado

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PlataformaPaqueteVersiones vulnerablesCorregida en
Python (PyPI) vllm < 0.8.0 0.8.0

Referencias

Puntuación CVSS 9.8/10
Vector CVSS
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
Debilidad (CWE)
CWE-1395
Plataforma
Python (PyPI)
Paquete
vllm
Publicada
23.04.2025
Actualizado
11.10.2026 14:19

Fuentes y licencias

OSV / GitHub Advisory Database - GHSA-ggpf-24jw-3fcw - CC BY 4.0

Historial de vulnerabilidades: vllm

Todas las vulnerabilidades publicadas de este paquete (Python (PyPI)), de la más reciente a la más antigua. Muestra si es un fallo aislado o un paquete que hay que vigilar.

Todas (94) en el archivo
Entradas en total
94
Primera → última
09.2024 10.2026
Sin corrección
5
Explotada en ataques
0

Distribución por gravedad

  • Crítica 9
  • Alta 23
  • Media 47
  • Baja 4
  • Sin evaluar 11

Divulgaciones por año

Gravedad:
  1. A flaw has been found in vllm-project vLLM up to 0.26.0. This vulnerability affects unknown code of the file rust/src/parser/src/unified/gemma4.rs of the component Gemma4UnifiedParser. Executing a man
    Alta · 7.5 CVE-2026-103241 < 0.27.0 0.27.0
  2. vLLM versions 0.22.0 through 0.23.0 fail to validate stop_token_ids against vocabulary bounds in Rust HTTP and gRPC frontends, allowing out-of-vocabulary token IDs to reach MinTokensLogitsProcessor. A
    Alta · 7.5 CVE-2026-100652 >= 0.22.0, < 0.24.0 0.24.0
  3. vLLM has Remote DoS via Invalid Recovered Token Reinjection
    Alta · 7.5 CVE-2026-54234 >= 0.17.1, < 0.24.0 0.24.0
  4. vLLM: ReDoS via structured_outputs.regex compiled without timeout in xgrammar and outlines backends
    Alta · 7.5 CVE-2026-55574 < 0.24.0 0.24.0
  5. vLLM denial of service via prompt embeds on M-RoPE models
    Alta CVE-2026-55514 >= 0.12.0, < 0.24.0 0.24.0
  6. vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.22.1, the vLLM Dockerfile is vulnerable to a dependency confusion attack through the flashinfer-jit-cache package.
    Alta · 8.8 CVE-2026-54232 < 0.22.1 0.22.1
  7. vLLM: OpenAI auth bypass
    Crítica · 9.1 CVE-2026-48746 >= 0.3.0, < 0.22.0 0.22.0
  8. vLLM: Security Check Bypass via assert Statement in Activation Function Loading Allows Arbitrary Code Execution
    Alta · 7.5 CVE-2026-41523 < 0.22.0 0.22.0
  9. vLLM versions 0.8.0 and later are vulnerable to an Out-of-Memory (OOM) Denial of Service (DoS) attack due to unbounded frame count processing in the `VideoMediaIO.load_base64()` method. When processin
    Alta · 7.5 CVE-2026-5497 >= 0.8.0, < 0.19.0 0.19.0
  10. vLLM has Hardcoded Trust Override in Model Files Enables RCE Despite Explicit User Opt-Out
    Alta · 8.8 CVE-2026-27893 >= 0.10.1, < 0.18.0 0.18.0
  11. vLLM has RCE In Video Processing
    Crítica · 9.8 CVE-2026-22778 >= 0.8.3, < 0.14.1 0.14.1
  12. vLLM vulnerable to Server-Side Request Forgery (SSRF) through MediaConnector
    Alta · 7.1 CVE-2026-24779 < 0.14.1 0.14.1
  13. vLLM affected by RCE via auto_map dynamic module loading during model initialization
    Alta · 8.8 CVE-2026-22807 >= 0.10.1, < 0.14.0 0.14.0
  14. vLLM introduced enhanced protection for CVE-2025-62164
    Alta · 8.8 CVE-2026-56340 >= 0.10.2, < 0.13.0 0.13.0
  15. vLLM vulnerable to remote code execution via transformers_utils/get_config
    Alta · 7.1 CVE-2025-66448 < 0.11.1 0.11.1
  16. vLLM vulnerable to DoS with incorrect shape of multimodal embedding inputs
    Alta · 6.5 CVE-2025-62372 >= 0.5.5, < 0.11.1 0.11.1
  17. vLLM deserialization vulnerability leading to DoS and potential RCE
    Alta · 8.8 CVE-2025-62164 >= 0.10.2, < 0.11.1 0.11.1
  18. vLLM is vulnerable to Server-Side Request Forgery (SSRF) through `MediaConnector` class
    Alta · 7.1 CVE-2025-6242 >= 0.5.0, < 0.11.0 0.11.0
  19. vLLM is vulnerable to timing attack at bearer auth
    Alta · 7.5 CVE-2025-59425 < 0.11.0 0.11.0
  20. vLLM has remote code execution vulnerability in the tool call parser for Qwen3-Coder
    Alta · 8.8 CVE-2025-9141 >= 0.10.0, < 0.10.1.1 0.10.1.1
  21. vllm API endpoints vulnerable to Denial of Service Attacks
    Alta · 7.5 CVE-2025-48956 >= 0.1.0, < 0.10.1.1 0.10.1.1
  22. vLLM Allows Remote Code Execution via PyNcclPipe Communication Service
    Crítica · 9.8 CVE-2025-47277 >= 0.6.5, < 0.8.5 0.8.5
  23. Remote Code Execution Vulnerability in vLLM Multi-Node Cluster Configuration
    Alta · 8.0 CVE-2025-30165 >= 0.5.2, < 0.10.0 0.10.0
  24. vLLM Vulnerable to Remote Code Execution via Mooncake Integration
    Crítica · 10.0 CVE-2025-32444 >= 0.6.5, < 0.8.5 0.8.5
  25. Data exposure via ZeroMQ on multi-node vLLM deployment
    Alta · 7.5 CVE-2025-30202 >= 0.5.2, < 0.8.5 0.8.5
  26. CVE-2025-24357 Malicious model remote code execution fix bypass with PyTorch < 2.6.0 esta entrada
    Crítica · 9.8 ghsa-ggpf-24jw-3fcw < 0.8.0 0.8.0
  27. vLLM deserialization vulnerability in vllm.distributed.GroupCoordinator.recv_object
    Crítica · 9.8 CVE-2024-9052 <= 0.8.1 sin corrección
  28. vLLM Deserialization of Untrusted Data vulnerability
    Crítica · 9.8 CVE-2024-11041 <= 0.6.2 sin corrección
  29. vLLM allows Remote Code Execution by Pickle Deserialization via AsyncEngineRPCServer() RPC server entrypoints
    Crítica · 9.8 CVE-2024-9053 <= 0.6.0 sin corrección
  30. vLLM Allows Remote Code Execution via Mooncake Integration
    Crítica · 9.0 CVE-2025-29783 >= 0.6.5, < 0.8.0 0.8.0
  31. vllm: Malicious model to RCE by torch.load in hf_model_weights_iterator
    Alta · 7.5 CVE-2025-24357 < 0.7.0 0.7.0
  32. vLLM denial of service vulnerability
    Alta · 7.5 CVE-2024-8768 < 0.5.5 0.5.5

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