Heap buffer overflow caused by rounding

### Impact An attacker can trigger a heap buffer overflow in `tf.raw_ops.QuantizedResizeBilinear` by manipulating input values so that float rounding results in off-by-one error in accessing image elements: ```python import tensorflow as tf l = [256, 328, 361, 17, 361, 361, 361, 361, 361, 361, 361, 361, 361, 361, 384] images = tf.constant(l, shape=[1, 1, 15, 1], dtype=tf.qint32) size = tf.constant([12, 6], shape=[2], dtype=tf.int32) min = tf.constant(80.22522735595703) max = tf.constant(80.39215850830078) tf.raw_ops.QuantizedResizeBilinear(images=images, size=size, min=min, max=max, align_corners=True, half_pixel_centers=True) ``` This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/44b7f486c0143f68b56c34e2d01e146ee445134a/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L62-L66) computes two integers (representing the upper and lower bounds for interpolation) by ceiling and flooring a floating point value: ```cc const float in_f = std::floor(in); interpolation->lower[i] = std::max(static_cast<int64>(in_f), static_cast<int64>(0)); interpolation->upper[i] = std::min(static_cast<int64>(std::ceil(in)), in_size - 1); ``` For some values of `in`, `interpolation->upper[i]` might be smaller than `interpolation->lower[i]`. This is an issue if `interpolation->upper[i]` is capped at `in_size-1` as it means that `interpolation->lower[i]` points outside of the image. Then, [in the interpolation code](https://github.com/tensorflow/tensorflow/blob/44b7f486c0143f68b56c34e2d01e146ee445134a/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L245-L264), this would result in heap buffer overflow: ```cc template <int RESOLUTION, typename T, typename T_SCALE, typename T_CALC> inline void OutputLerpForChannels(const InterpolationCache<T_SCALE>& xs, const int64 x, const T_SCALE ys_ilerp, const int channels, const float min, const float max, const T* ys_input_lower_ptr, const T* ys_input_upper_ptr, T* output_y_ptr) { const int64 xs_lower = xs.lower[x]; ... for (int c = 0; c < channels; ++c) { const T top_left = ys_input_lower_ptr[xs_lower + c]; ... } } ``` For the other cases where `interpolation->upper[i]` is smaller than `interpolation->lower[i]`, we can set them to be equal without affecting the output. ### Patches We have patched the issue in GitHub commit [f851613f8f0fb0c838d160ced13c134f778e3ce7](https://github.com/tensorflow/tensorflow/commit/f851613f8f0fb0c838d160ced13c134f778e3ce7). 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 Ying Wang and Yakun Zhang 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-131, CWE-193

Also known as

BIT-tensorflow-2021-29529, CVE-2021-29529, PYSEC-2021-166, PYSEC-2021-457, PYSEC-2021-655

• github.com/tensorflow/tensorflow/security/advisories/GHSA-jfp7-4j67-8r3q
• nvd.nist.gov/vuln/detail/CVE-2021-29529
• github.com/tensorflow/tensorflow/commit/f851613f8f0fb0c838d160ced13c134f778e3ce7
• github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-457.yaml
• github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-655.yaml
• github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-166.yaml
• github.com/tensorflow/tensorflow