CVE-2020-15193

Memory corruption in Tensorflow

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Attribution

This vulnerability has been reported by members of the Aivul Team from Qihoo 360.

The implementation of dlpack.to_dlpack can be made to use uninitialized memory resulting in further memory corruption. This is because the pybind11 glue code assumes that the argument is a tensor:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/python/tfe_wrapper.cc#L1361

However, there is nothing stopping users from passing in a Python object instead of a tensor.

In [2]: tf.experimental.dlpack.to_dlpack([2])                                                                                                                                            
==1720623==WARNING: MemorySanitizer: use-of-uninitialized-value                                                                                                                            
    #0 0x55b0ba5c410a in tensorflow::(anonymous namespace)::GetTensorFromHandle(TFE_TensorHandle*, TF_Status*) third_party/tensorflow/c/eager/dlpack.cc:46:7
    #1 0x55b0ba5c38f4 in tensorflow::TFE_HandleToDLPack(TFE_TensorHandle*, TF_Status*) third_party/tensorflow/c/eager/dlpack.cc:252:26
... 

The uninitialized memory address is due to a reinterpret_cast
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/python/eager/pywrap_tensor.cc#L848-L850

Since the PyObject is a Python object, not a TensorFlow Tensor, the cast to EagerTensor fails.

CVE-2020-15193 has a CVSS score of 7.1 (High). The vector is network-reachable, low privileges required, and no user interaction. A CVSS score reflects the worst-case severity of the vulnerability, not your specific exposure. Whether this affects your application depends on whether the vulnerable code is present and reachable in your environment. A fixed version is available (2.2.1, 2.3.1); upgrading removes the vulnerable code path.

Kodem intelligence

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We have patched the issue in 22e07fb204386768e5bcbea563641ea11f96ceb8 and will release a patch release for all affected versions.

We recommend users to upgrade to TensorFlow 2.2.1 or 2.3.1.

1. What is CVE-2020-15193? CVE-2020-15193 is a high-severity security vulnerability in tensorflow (pip), affecting versions = 2.2.0. It is fixed in 2.2.1, 2.3.1.
2. How severe is CVE-2020-15193? CVE-2020-15193 has a CVSS score of 7.1 (High). This score reflects the worst-case severity of the vulnerability, not your specific exposure. Whether it represents real risk in your environment depends on whether the vulnerable code is present and reachable.
3. Which packages are affected by CVE-2020-15193?
   • tensorflow (pip) (versions = 2.2.0)
   • tensorflow-cpu (pip) (versions = 2.2.0)
   • tensorflow-gpu (pip) (versions = 2.2.0)
4. Is there a fix for CVE-2020-15193? Yes. CVE-2020-15193 is fixed in 2.2.1, 2.3.1. Upgrade to this version or later.
5. Is CVE-2020-15193 exploitable, and should I be worried? Whether CVE-2020-15193 is exploitable in your environment depends on whether the vulnerable code is present and reachable. A CVSS score is a worst-case rating; it does not account for your specific deployment, configuration, or usage patterns. Kodem, an Intelligent Application Security platform, uses runtime intelligence to show which vulnerabilities actually execute in production, so you can focus on the ones that represent real risk. Get a demo
6. What actually determines whether CVE-2020-15193 is exploitable, and how bad it is? Exploitability and impact are not fixed properties of a CVE. They depend on runtime truth: whether the vulnerable code is present, reachable, and actually executes in your application. A high CVSS score on a dependency that never runs is not the same as real risk. Kodem, an Intelligent Application Security platform, uses runtime intelligence to reveal which vulnerabilities actually execute in production, so teams prioritize the ones that genuinely matter.
7. How do I fix CVE-2020-15193?
   • Upgrade tensorflow to 2.2.1 or later
   • Upgrade tensorflow to 2.3.1 or later
   • Upgrade tensorflow-cpu to 2.2.1 or later
   • Upgrade tensorflow-cpu to 2.3.1 or later
   • Upgrade tensorflow-gpu to 2.2.1 or later
   • Upgrade tensorflow-gpu to 2.3.1 or later

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