CVE-2026-73558 in vLLM
Summary
by MITRE • 08/13/2026
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.
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Analysis
by VulDB Data Team • 08/13/2026
The vulnerability exists within vLLM's inference and serving engine for large language models where an integer overflow condition occurs in the activation_kernels.cu file at line involving blockIdx.x 2 d calculations. This flaw represents a critical security issue that falls under CWE-191 Integer Underflow/Overflow, specifically manifesting as an integer overflow during kernel execution. The vulnerability affects versions prior to 0.27.0 and stems from improper handling of batched processing operations where multiple user requests are processed concurrently within the same inference batch.
The technical implementation flaw manifests when the act_and_mul_kernel function processes multiple user inputs simultaneously, causing the multiplication operation blockIdx.x 2 d to exceed the maximum representable value for the data type used. This overflow condition results in incorrect memory addressing and data pointer calculations that allow one user's input data to be inadvertently consumed by another user's processing pipeline. The vulnerability specifically impacts batched inference scenarios where multiple requests are processed together, creating a cross-contamination of user data between concurrent operations.
Operationally, this vulnerability creates a severe privacy and data integrity risk for users of the vLLM platform. A malicious actor or compromised system could potentially access partial or complete copies of other users' inference results during batch processing operations. This represents a data leakage scenario that violates fundamental security principles of isolation and confidentiality in multi-tenant environments. The impact extends beyond simple information disclosure to potentially enabling more sophisticated attacks such as prompt injection or model inversion techniques that rely on accessing specific user inputs. Attackers could exploit this vulnerability through carefully crafted batched requests to harvest sensitive information from other users' queries.
The mitigation strategy involves upgrading to vLLM version 0.27.0 or later where the integer overflow has been corrected through proper bounds checking and data type validation in the activation_kernels.cu file. Security practitioners should also implement monitoring for anomalous batch processing behaviors and consider additional isolation measures when processing sensitive user data. Organizations using vLLM should conduct thorough security assessments of their inference pipelines, particularly focusing on batched processing operations that handle multiple concurrent user requests. This vulnerability demonstrates the importance of proper integer handling in GPU kernel implementations and aligns with ATT&CK technique T1580 for exploitation of vulnerabilities in cloud infrastructure components. The fix addresses the root cause by ensuring that batch indexing calculations remain within valid ranges, preventing cross-contamination between user requests during parallel processing operations.
This vulnerability highlights the critical security considerations for large language model serving platforms that process multiple concurrent user requests, emphasizing the need for robust input validation and memory safety measures in high-performance computing environments where integer overflows can lead to serious data integrity compromises.