Uninitialized Memory Exposure Affecting tensorflow-cpu package, versions [, 2.1.3)[2.2.0, 2.2.2)[2.3.0, 2.3.2)
Threat Intelligence
Snyk has a proof-of-concept or detailed explanation of how to exploit this vulnerability.
The probability is the direct output of the EPSS model, and conveys an overall sense of the threat of exploitation in the wild. The percentile measures the EPSS probability relative to all known EPSS scores. Note: This data is updated daily, relying on the latest available EPSS model version. Check out the EPSS documentation for more details.
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Test your applications- Snyk IDSNYK-PYTHON-TENSORFLOWCPU-1050414
- published11 Dec 2020
- disclosed10 Dec 2020
- creditUnknown
Introduced: 10 Dec 2020
CVE-2020-26271 (opens in a new tab)How to fix?
Upgrade tensorflow-cpu to version 2.1.3, 2.2.2, 2.3.2 or higher.
Overview
tensorflow-cpu is a machine learning framework.
Affected versions of this package are vulnerable to Uninitialized Memory Exposure. 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.