8.8

CVSS Score

3.1

-

CVSS Score

Basic Information

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GHSA-mcmc-2m55-j8jj

GHSA-mcmc-2m55-j8jj: vLLM introduced enhanced protection for CVE-2025-62164

Summary

The fix here for CVE-2025-62164 is not sufficient. The fix only disables prompt embeds by default rather than addressing the root cause, so the DoS vulnerability remains when the feature is enabled.

Details

vLLM's pending change attempts to fix the root cause, which is the missing sparse tensor validation. PyTorch (~v2.0) disables sparse tensor validation (specifically, sparse tensor invariants checks) by default for performance reasons. vLLM is adding the sparse tensor validation to ensure indices are valid, non-negative, and within bounds. These checks help catch malformed tensors.

PoC

Impact

Current fix only added a flag to disable/enable prompt embeds, so by default, prompt embeds feature is disabled in vLLM, which stops DoS attacks through the embeddings. However, It doesn’t address the problem when the flag is enabled and there is still potential for DoS attacks.

Changes

https://github.com/vllm-project/vllm/pull/30649

(GitHub Advisory)

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GHSA-mcmc-2m55-j8jj

GHSA-mcmc-2m55-j8jj:

8.8

CVSS Score

3.1

-

CVSS Score

Basic Information

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Technical Details

Package Name	Ecosystem	Vulnerable Versions	First Patched Version
vllm	pip	>= 0.10.2, < 0.11.1	0.13.0

Technical Details

Vulnerability Intelligence
Miggo AI

Root Cause Analysis

The vulnerability is a Denial of Service (DoS) caused by insufficient validation of sparse tensors when processing prompt embeddings. The root cause is that PyTorch, for performance reasons, does not validate sparse tensor invariants by default. An attacker can provide a maliciously crafted sparse tensor with out-of-bounds indices. When the application attempts to convert this sparse tensor to a dense tensor using .to_dense(), it can result in an out-of-bounds memory write, leading to a process crash.

The patch addresses this by wrapping the tensor loading and conversion logic within a torch.sparse.check_sparse_tensor_invariants() context manager. This forces PyTorch to validate the sparse tensor's indices, preventing the out-of-bounds write from occurring.

The vulnerable functions are all entry points where external data is deserialized into PyTorch tensors to be used as embeddings. This includes functions for handling prompt embeddings in the completions API, as well as image and audio embeddings in the multimodal APIs, whether loaded from a file, raw bytes, or a base64 encoded string.

Vulnerable functions

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WAF Protection Rules

WAF Rule

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Reasoning

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8.8

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Basic Information

Basic Information

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GHSA-mcmc-2m55-j8jj: vLLM introduced enhanced protection for CVE-2025-62164

Summary

Details

PoC

Impact

Changes

GHSA-mcmc-2m55-j8jj:

8.8

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Basic Information

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Technical Details

Technical Details

Vulnerability IntelligenceMiggo AI

Root Cause Analysis

Vulnerable functions

Vulnerability IntelligenceMiggo AI

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WAF Protection Rules

WAF Rule

Reasoning

8.8

8.8

Basic Information

Basic Information

GHSA-mcmc-2m55-j8jj: vLLM introduced enhanced protection for CVE-2025-62164

Summary

Details

PoC

Impact

Changes

Technical Details

Vulnerability IntelligenceMiggo AI

Root Cause Analysis

Vulnerable functions

Vulnerability Intelligence
Miggo AI

Vulnerability Intelligence
Miggo AI

Vulnerability Intelligence
Miggo AI