CHECK-fail in DrawBoundingBoxes

### Impact An attacker can trigger a denial of service via a `CHECK` failure by passing an empty image to `tf.raw_ops.DrawBoundingBoxes`: ```python import tensorflow as tf images = tf.fill([53, 0, 48, 1], 0.) boxes = tf.fill([53, 31, 4], 0.) boxes = tf.Variable(boxes) boxes[0, 0, 0].assign(3.90621) tf.raw_ops.DrawBoundingBoxes(images=images, boxes=boxes) ``` This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/ea34a18dc3f5c8d80a40ccca1404f343b5d55f91/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L148-L165) uses `CHECK_*` assertions instead of `OP_REQUIRES` to validate user controlled inputs. Whereas `OP_REQUIRES` allows returning an error condition back to the user, the `CHECK_*` macros result in a crash if the condition is false, similar to `assert`. ```cc const int64 max_box_row_clamp = std::min<int64>(max_box_row, height - 1); ... CHECK_GE(max_box_row_clamp, 0); ``` In this case, `height` is 0 from the `images` input. This results in `max_box_row_clamp` being negative and the assertion being falsified, followed by aborting program execution. ### Patches We have patched the issue in GitHub commit [b432a38fe0e1b4b904a6c222cbce794c39703e87](https://github.com/tensorflow/tensorflow/commit/b432a38fe0e1b4b904a6c222cbce794c39703e87). The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, 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 Yakun Zhang and Ying Wang of Baidu X-Team.

CVSS 3.1

CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L

Severity from

GitHub (reviewed advisory)

Weakness

CWE-754

Also known as

BIT-tensorflow-2021-29533, CVE-2021-29533, PYSEC-2021-170, PYSEC-2021-461, PYSEC-2021-659

• github.com/tensorflow/tensorflow/security/advisories/GHSA-393f-2jr3-cp69
• nvd.nist.gov/vuln/detail/CVE-2021-29533
• github.com/tensorflow/tensorflow/commit/b432a38fe0e1b4b904a6c222cbce794c39703e87
• github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-461.yaml
• github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-659.yaml
• github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-170.yaml
• github.com/tensorflow/tensorflow