Heap out of bounds access in MakeEdge in TensorFlow

In affected versions of TensorFlow under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The MakeEdge function creates an edge between one output tensor of the src node (given by output_index) and the input slot of the dst node (given by input_index). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding DataType values and comparing these for equality. However, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays. In most scenarios, this can manifest as unitialized data access, but if the index points far away from the boundaries of the arrays this can be used to leak addresses from the library. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.

CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L

跨界内存读

Google TensorFlow 缓冲区错误漏洞

Google TensorFlow是美国谷歌（Google）公司的一套用于机器学习的端到端开源平台。 TensorFlow存在缓冲区错误漏洞，该漏洞源于MakeEdge函数在src节点的一个输出张量（由output_index给出）和dst节点的输入槽（由input_index给出）之间创建一条边。仅当两侧张量的类型一致时才有可能，因此该函数从获取对应的DataType值并将它们进行相等性比较开始。但是，不检查索引是否指向它们索引到的数组的内部。因此，这可能导致访问数据超出相应的堆分配数组的范围。在大多数

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缓冲区错误