CVE-2021-37670: Heap OOB in `UpperBound` and `LowerBound` in TensorFlow
First published: Thu Aug 12 2021(Updated: )
### Impact An attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to `tf.raw_ops.UpperBound`: ```python import tensorflow as tf tf.raw_ops.UpperBound( sorted_input=[1,2,3], values=tf.constant(value=[[0,0,0],[1,1,1],[2,2,2]],dtype=tf.int64), out_type=tf.int64) ``` The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/searchsorted_op.cc#L85-L104) does not validate the rank of `sorted_input` argument: ```cc void Compute(OpKernelContext* ctx) override { const Tensor& sorted_inputs_t = ctx->input(0); // ... OP_REQUIRES(ctx, sorted_inputs_t.dim_size(0) == values_t.dim_size(0), Status(error::INVALID_ARGUMENT, "Leading dim_size of both tensors must match.")); // ... if (output_t->dtype() == DT_INT32) { OP_REQUIRES(ctx, FastBoundsCheck(sorted_inputs_t.dim_size(1), ...)); // ... } ``` As we access the first two dimensions of `sorted_inputs_t` tensor, it must have rank at least 2. A similar issue occurs in `tf.raw_ops.LowerBound`. ### Patches We have patched the issue in GitHub commit [42459e4273c2e47a3232cc16c4f4fff3b3a35c38](https://github.com/tensorflow/tensorflow/commit/42459e4273c2e47a3232cc16c4f4fff3b3a35c38). The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. ### For more information Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. ### Attribution This vulnerability has been reported by members of the Aivul Team from Qihoo 360.
Credit: security-advisories@github.com security-advisories@github.com
| Affected Software | Affected Version | How to fix |
|---|---|---|
| Google TensorFlow | >=2.3.0<2.3.4 | |
| Google TensorFlow | >=2.4.0<2.4.3 | |
| Google TensorFlow | =2.5.0 | |
| Google TensorFlow | =2.6.0-rc0 | |
| Google TensorFlow | =2.6.0-rc1 | |
| Google TensorFlow | =2.6.0-rc2 | |
| pip/tensorflow-gpu | =2.5.0 | 2.5.1 |
| pip/tensorflow-gpu | >=2.4.0<2.4.3 | 2.4.3 |
| pip/tensorflow-gpu | <2.3.4 | 2.3.4 |
| pip/tensorflow-cpu | =2.5.0 | 2.5.1 |
| pip/tensorflow-cpu | >=2.4.0<2.4.3 | 2.4.3 |
| pip/tensorflow-cpu | <2.3.4 | 2.3.4 |
| pip/tensorflow | =2.5.0 | 2.5.1 |
| pip/tensorflow | >=2.4.0<2.4.3 | 2.4.3 |
| pip/tensorflow | <2.3.4 | 2.3.4 |
| >=2.3.0<2.3.4 | ||
| >=2.4.0<2.4.3 | ||
| =2.5.0 | ||
| =2.6.0-rc0 | ||
| =2.6.0-rc1 | ||
| =2.6.0-rc2 |
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Reference Links
- https://github.com/tensorflow/tensorflow/commit/42459e4273c2e47a3232cc16c4f4fff3b3a35c38
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9697-98pf-4rw7
- https://nvd.nist.gov/vuln/detail/CVE-2021-37670
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-583.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-781.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-292.yaml
- https://github.com/advisories/GHSA-9697-98pf-4rw7 (Advisory)
Frequently Asked Questions
What is the severity of CVE-2021-37670?
CVE-2021-37670 has a severity rating that indicates a potential risk of heap memory corruption due to out-of-bounds read.
How do I fix CVE-2021-37670?
To address CVE-2021-37670, upgrade to TensorFlow versions 2.5.1 or 2.4.3, or apply the latest patches available.
Which versions of TensorFlow are affected by CVE-2021-37670?
CVE-2021-37670 affects TensorFlow versions 2.3.0 to 2.3.4, 2.4.0 to 2.4.3, and specific release candidates of 2.6.0.
What type of attack is possible with CVE-2021-37670?
An attacker can exploit CVE-2021-37670 by sending specially crafted illegal arguments to the `tf.raw_ops.UpperBound` operation.
Is CVE-2021-37670 related to any specific functionality in TensorFlow?
Yes, CVE-2021-37670 is related to the operation of bounds checking in the `tf.raw_ops.UpperBound` function.
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