CVE-2021-29521 Segfault in SparseCountSparseOutput

2021-05-1419:35:49

CWE-131

GitHub_M

www.cve.org

2.5 Low

CVSS3

Attack Vector

LOCAL

Attack Complexity

HIGH

Privileges Required

LOW

User Interaction

NONE

Scope

UNCHANGED

Confidentiality Impact

NONE

Integrity Impact

NONE

Availability Impact

LOW

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

0.0004 Low

EPSS

Percentile

12.8%

JSON

TensorFlow is an end-to-end open source platform for machine learning. Specifying a negative dense shape in tf.raw_ops.SparseCountSparseOutput results in a segmentation fault being thrown out from the standard library as std::vector invariants are broken. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L199-L213) assumes the first element of the dense shape is always positive and uses it to initialize a BatchedMap<T> (i.e., std::vector<absl::flat_hash_map<int64,T>>(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L27)) data structure. If the shape tensor has more than one element, num_batches is the first value in shape. Ensuring that the dense_shape argument is a valid tensor shape (that is, all elements are non-negative) solves this issue. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3.

CNA Affected

[
  {
    "product": "tensorflow",
    "vendor": "tensorflow",
    "versions": [
      {
        "status": "affected",
        "version": "< 2.3.3"
      },
      {
        "status": "affected",
        "version": ">= 2.4.0, < 2.4.2"
      }
    ]
  }
]