Vulnerability · Published May 14, 2021 · Assigned by GitHub_M
CVE-2021-29531CHECK-fail in tf.raw_ops.EncodePng
Low 2.5
In plain English
A low-severity security flaw in tensorflow from tensorflow. There are no confirmed reports of exploitation so far. The chance it is exploited in the next 30 days is estimated at 0.2% (higher than 9% of all known vulnerabilities). Fixed versions are available - upgrade the affected package.
Official description
TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a `CHECK` fail in PNG encoding by providing an empty input tensor as the pixel data. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L57-L60) only validates that the total number of pixels in the image does not overflow. Thus, an attacker can send an empty matrix for encoding. However, if the tensor is empty, then the associated buffer is `nullptr`. Hence, when calling `png::WriteImageToBuffer`(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L79-L93), the first argument (i.e., `image.flat<T>().data()`) is `NULL`. This then triggers the `CHECK_NOTNULL` in the first line of `png::WriteImageToBuffer`(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/lib/png/png_io.cc#L345-L349). Since `image` is null, this results in `abort` being called after printing the stacktrace. Effectively, this allows an attacker to mount a denial of service attack. 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.
Affected packages & fixed versions
| Ecosystem | Package | Fixed in | Advisory |
|---|---|---|---|
| PyPI | tensorflow | 2.3.3 | GHSA-3qxp-qjq7-w4hf |
| PyPI | tensorflow | 2.4.2 | GHSA-3qxp-qjq7-w4hf |
| PyPI | tensorflow | 2.1.4 | GHSA-3qxp-qjq7-w4hf |
| PyPI | tensorflow | 2.1.4, 2.2.3, 2.3.3, 2.4.2 | PYSEC-2021-168 |
| PyPI | tensorflow | 2.2.3 | GHSA-3qxp-qjq7-w4hf |
| PyPI | tensorflow-cpu | 2.4.2 | GHSA-3qxp-qjq7-w4hf |
| PyPI | tensorflow-cpu | 2.1.4 | GHSA-3qxp-qjq7-w4hf |
| PyPI | tensorflow-cpu | 2.2.3 | GHSA-3qxp-qjq7-w4hf |
| PyPI | tensorflow-cpu | 2.3.3 | GHSA-3qxp-qjq7-w4hf |
| PyPI | tensorflow-cpu | 2.1.4, 2.2.3, 2.3.3, 2.4.2 | PYSEC-2021-459 |
| PyPI | tensorflow-gpu | 2.3.3 | GHSA-3qxp-qjq7-w4hf |
| PyPI | tensorflow-gpu | 2.4.2 | GHSA-3qxp-qjq7-w4hf |
| PyPI | tensorflow-gpu | 2.1.4 | GHSA-3qxp-qjq7-w4hf |
| PyPI | tensorflow-gpu | 2.1.4, 2.2.3, 2.3.3, 2.4.2 | PYSEC-2021-657 |
| PyPI | tensorflow-gpu | 2.2.3 | GHSA-3qxp-qjq7-w4hf |
Affected products (as reported)
| Vendor | Product | Versions |
|---|---|---|
| tensorflow | tensorflow | < 2.1.4>= 2.2.0, < 2.2.3>= 2.3.0, < 2.3.3>= 2.4.0, < 2.4.2 |
Weakness: CWE-754 · Vector: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L