CVE-2020-15196 Heap buffer overflow in Tensorflow
Heap buffer overflow in TensorFlow allows attackers to cause crashes or execute code via malformed sparse tensor inputs.
- Exploited in the wild
- Not known
- Login needed
- Any signed-in user
- Attackable
- Over the internet
- Victim action needed
- Not needed
- Fix
- Available
What is CVE-2020-15196?
CVE-2020-15196 is a high-severity memory corruption vulnerability in Google Tensorflow, published on 25 Sep 2020. Any signed-in user can exploit it over the network, with no action from the victim.
Official description
In Tensorflow version 2.3.0, the `SparseCountSparseOutput` and `RaggedCountSparseOutput` implementations don't validate that the `weights` tensor has the same shape as the data. The check exists for `DenseCountSparseOutput`, where both tensors are fully specified. In the sparse and ragged count weights are still accessed in parallel with the data. But, since there is no validation, a user passing fewer weights than the values for the tensors can generate a read from outside the bounds of the heap buffer allocated for the weights. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.
Which versions are affected by CVE-2020-15196?
10 open source packages ship an affected version, including tensorflow, tensorflow-cpu and tensorflow-gpu. It affects 1 product.
| Package | Affected | Fixed in |
|---|---|---|
tensorflow Bitnami | From 2.3.0 | 2.3.1 |
tensorflow PyPI | All versions | 2.3.1 |
tensorflow PyPI | From 2.3.0 | 2.3.1 |
tensorflow PyPI | From 2.3.0 | 2.3.1 |
tensorflow-cpu PyPI | All versions | 2.3.1 |
tensorflow-cpu PyPI | From 2.3.0 | 2.3.1 |
4 more packages
tensorflow-cpu PyPI | From 2.3.0 | 2.3.1 |
tensorflow-gpu PyPI | All versions | 2.3.1 |
tensorflow-gpu PyPI | From 2.3.0 | 2.3.1 |
tensorflow-gpu PyPI | From 2.3.0 | 2.3.1 |
Products include Google Tensorflow.
How do I fix CVE-2020-15196?
Upgrade tensorflow to 2.3.1 or later.
Upgrade to the fixed version. Memory-safe languages, compiler hardening and fuzzing prevent most new cases. More about Memory corruption.
Is CVE-2020-15196 being exploited?
There is no known exploitation of CVE-2020-15196 in the wild and no public exploit code.
Other vulnerabilities in Google Tensorflow
- CVE-2023-25668 TensorFlow vulnerable to heap out-of-buffer read in the QuantizeAndDequantize operation
- CVE-2018-7575 Integer Overflow or Wraparound in Google TensorFlow
- CVE-2021-37678 Arbitrary code execution due to YAML deserialization
- CVE-2021-35958 Path traversal in n/a
- CVE-2020-15202 Integer truncation in Shard API usage
Sources
Also known as BIT-tensorflow-2020-15196, GHSA-pg59-2f92-5cph, PYSEC-2020-119, PYSEC-2020-276, PYSEC-2020-311.