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    <lastBuildDate>Sat, 03 Oct 2026 01:18:09 +0000</lastBuildDate>
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      <title>CVE-2026-53923 — vLLM GGUF Kernels: int64_t to int truncation of tensor dimensions causes GPU buffer overflow</title>
      <link>https://cve.radiocsirt.org/vuln/cve-2026-53923</link>
      <description>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; vllm-project vllm&lt;/p&gt;
&lt;p&gt;vLLM is an inference and serving engine for large language models (LLMs). From 0.5.5 until 0.23.1rc0, integer truncation of tensor dimensions in vLLM&amp;#39;s GGUF dequantize kernels (csrc/quantization/gguf/gguf_kernel.cu) causes partial tensor processing. The output tensor is allocated at full size via torch::empty (uninitialized memory), but the dequantize CUDA kernel processes only a truncated number of elements. The unfilled portion of the output tensor retains whatever was previously in GPU memory. In multi-tenant inference deployments, this residual GPU memory may contain tensor data from other users&amp;#39; inference requests, constituting information disclosure. This vulnerability is fixed in 0.23.1rc0.&lt;/p&gt;</description>
      <content:encoded>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; vllm-project vllm&lt;/p&gt;
&lt;p&gt;vLLM is an inference and serving engine for large language models (LLMs). From 0.5.5 until 0.23.1rc0, integer truncation of tensor dimensions in vLLM&amp;#39;s GGUF dequantize kernels (csrc/quantization/gguf/gguf_kernel.cu) causes partial tensor processing. The output tensor is allocated at full size via torch::empty (uninitialized memory), but the dequantize CUDA kernel processes only a truncated number of elements. The unfilled portion of the output tensor retains whatever was previously in GPU memory. In multi-tenant inference deployments, this residual GPU memory may contain tensor data from other users&amp;#39; inference requests, constituting information disclosure. This vulnerability is fixed in 0.23.1rc0.&lt;/p&gt;</content:encoded>
      <guid isPermaLink="false">https://cve.radiocsirt.org/vuln/cve-2026-53923</guid>
    </item>
    <item>
      <title>GHSA-5jv2-g5wq-cmr4 — vLLM: GGUF dequantize kernel int truncation exposes uninitialized GPU memory in multi-tenant serving</title>
      <link>https://cve.radiocsirt.org/vuln/ghsa-5jv2-g5wq-cmr4</link>
      <description>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; PyPI: vllm&lt;/p&gt;
&lt;p&gt;## Summary&lt;/p&gt;
&lt;p&gt;Integer truncation of tensor dimensions in vLLM&amp;#39;s GGUF dequantize kernels (`csrc/quantization/gguf/gguf_kernel.cu`) causes partial tensor processing. The output tensor is allocated at full size via `torch::empty` (uninitialized memory), but the dequantize CUDA kernel processes only a truncated number of elements. The unfilled portion of the output tensor retains whatever was previously in GPU memory. In multi-tenant inference deployments, this residual GPU memory may contain tensor data from other users&amp;#39; inference requests, constituting information disclosure.&lt;/p&gt;
&lt;p&gt;## Root Cause&lt;/p&gt;
&lt;p&gt;The `to_cuda_ggml_t` function pointer type at `ggml-common.h:1067` declares its element count parameter as `int` (32-bit):&lt;/p&gt;
&lt;p&gt;```cpp
using to_cuda_ggml_t = void (*)(const void * __restrict__ x,
                                dst_t * __restrict__ y,
                                int k,              // 32-bit
                                cudaStream_t stream);
```&lt;/p&gt;
&lt;p&gt;All dequantize kernel functions (`dequantize_block_cuda`, `dequantize_row_q2_K_cuda`, etc. in `dequantize.cuh`) inherit this `int k` parameter and use it as the kernel launch grid size:&lt;/p&gt;
&lt;p&gt;```cpp
static void dequantize_block_cuda(..., const int k, cudaStream_t stream) {
    const int num_blocks = (k + 2*CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / (2*CUDA_DEQUANTIZE_BLOCK_SIZE);
    dequantize_block&amp;lt;&amp;lt;&amp;lt;num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream&amp;gt;&amp;gt;&amp;gt;(vx, y, k);
}
```&lt;/p&gt;
&lt;p&gt;In `ggml_dequantize()` at `gguf_kernel.cu:85`, the caller passes `m * n`…&lt;/p&gt;</description>
      <content:encoded>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; PyPI: vllm&lt;/p&gt;
&lt;p&gt;## Summary&lt;/p&gt;
&lt;p&gt;Integer truncation of tensor dimensions in vLLM&amp;#39;s GGUF dequantize kernels (`csrc/quantization/gguf/gguf_kernel.cu`) causes partial tensor processing. The output tensor is allocated at full size via `torch::empty` (uninitialized memory), but the dequantize CUDA kernel processes only a truncated number of elements. The unfilled portion of the output tensor retains whatever was previously in GPU memory. In multi-tenant inference deployments, this residual GPU memory may contain tensor data from other users&amp;#39; inference requests, constituting information disclosure.&lt;/p&gt;
&lt;p&gt;## Root Cause&lt;/p&gt;
&lt;p&gt;The `to_cuda_ggml_t` function pointer type at `ggml-common.h:1067` declares its element count parameter as `int` (32-bit):&lt;/p&gt;
&lt;p&gt;```cpp
using to_cuda_ggml_t = void (*)(const void * __restrict__ x,
                                dst_t * __restrict__ y,
                                int k,              // 32-bit
                                cudaStream_t stream);
```&lt;/p&gt;
&lt;p&gt;All dequantize kernel functions (`dequantize_block_cuda`, `dequantize_row_q2_K_cuda`, etc. in `dequantize.cuh`) inherit this `int k` parameter and use it as the kernel launch grid size:&lt;/p&gt;
&lt;p&gt;```cpp
static void dequantize_block_cuda(..., const int k, cudaStream_t stream) {
    const int num_blocks = (k + 2*CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / (2*CUDA_DEQUANTIZE_BLOCK_SIZE);
    dequantize_block&amp;lt;&amp;lt;&amp;lt;num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream&amp;gt;&amp;gt;&amp;gt;(vx, y, k);
}
```&lt;/p&gt;
&lt;p&gt;In `ggml_dequantize()` at `gguf_kernel.cu:85`, the caller passes `m * n`…&lt;/p&gt;</content:encoded>
      <guid isPermaLink="false">https://cve.radiocsirt.org/vuln/ghsa-5jv2-g5wq-cmr4</guid>
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