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CVE-2026-73558: vLLM: Cross-User Data Leak Vulnerability

• Severity: medium (CVSS 5.3 v3.1)
• Exploited in the wild: not listed in CISA KEV
• EPSS: 0.4% probability of exploitation in the next 30 days
• Public exploit or detection template: yes
• Published: 2026-08-13 · updated 2026-09-28
• Weakness: CWE-190

Description

vLLM is an inference and serving engine for large language models. Prior to 0.27.0, an integer overflow in blockIdx.x * 2 * d in activation_kernels.cu can cause act_and_mul_kernel to consume another batched user's input, allowing a request processed in the same inference batch to receive a partial or complete copy of another user's inference result. This issue is fixed in version 0.27.0.

Affected products

• vllm-project vllm: before 0.27.0 — fixed in 0.27.0

How to fix

Upgrade to a fixed version: vllm-project vllm 0.27.0. Check the vendor advisory for mitigations.

References

• https://github.com/vllm-project/vllm/commit/451227cb3ff07989698fed982c2d3e4300257924
• https://github.com/vllm-project/vllm/issues/42860
• https://github.com/vllm-project/vllm/pull/49660
• https://github.com/vllm-project/vllm/releases/tag/v0.27.0
• https://github.com/vllm-project/vllm/security/advisories/GHSA-7m6h-x95x-82q5
• https://github.com/vllm-project/vllm/security/advisories/GHSA-7m6h-x95x-82q5

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