Heap OOB in `UpperBound` and `LowerBound`
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...
Affected versions
| Package | Affected | Fixed in |
|---|---|---|
| tensorflow PyPI | < 2.3.4 | 2.3.4 |
| >= 2.4.0, < 2.4.3 | 2.4.3 | |
| >= 2.5.0, < 2.5.1 | 2.5.1 |
Details and references
### 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.
- CVSS 3.1
- CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:N
- Severity from
- GitHub (reviewed advisory)
- Weakness
- CWE-125
- Also known as
- BIT-tensorflow-2021-37670, CVE-2021-37670, PYSEC-2021-292, PYSEC-2021-583, PYSEC-2021-781
- github.com/tensorflow/tensorflow/security/advisories/GHSA-9697-98pf-4rw7
- nvd.nist.gov/vuln/detail/CVE-2021-37670
- github.com/tensorflow/tensorflow/commit/42459e4273c2e47a3232cc16c4f4fff3b3a35c38
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-583.yaml
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-781.yaml
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-292.yaml
- github.com/tensorflow/tensorflow