CVE-2020-15200 Segfault in Tensorflow
An attacker can cause a segmentation fault and denial of service in TensorFlow by providing invalid ragged tensor inputs to RaggedCountSparseOutput.
- Exploited in the wild
- Not known
- Login needed
- Not needed
- Attackable
- Over the internet
- Victim action needed
- Not needed
- Fix
- Available
What is CVE-2020-15200?
CVE-2020-15200 is a medium-severity memory corruption vulnerability in Google Tensorflow, published on 25 Sep 2020. An attacker can exploit it over the internet without logging in, with no action from the victim.
Official description
In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the `splits` tensor generate a valid partitioning of the `values` tensor. Thus, the code sets up conditions to cause a heap buffer overflow. A `BatchedMap` is equivalent to a vector where each element is a hashmap. However, if the first element of `splits_values` is not 0, `batch_idx` will never be 1, hence there will be no hashmap at index 0 in `per_batch_counts`. Trying to access that in the user code results in a segmentation fault. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.
Which versions are affected by CVE-2020-15200?
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-2020-15200?
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-2020-15200 being exploited?
There is no known exploitation of CVE-2020-15200 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-2020-15200, GHSA-x7rp-74x2-mjf3, PYSEC-2020-123, PYSEC-2020-280, PYSEC-2020-315.