CVE-2020-15196 Heap buffer overflow in Tensorflow

2020-09-2518:40:36

CWE-119

CWE-122

GitHub_M

www.cve.org

tensorflow

heap buffer overflow

cve-2020-15196

validation

patched

commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02

CVSS3

8.5

Attack Vector

NETWORK

Attack Complexity

HIGH

Privileges Required

LOW

User Interaction

NONE

Scope

CHANGED

Confidentiality Impact

HIGH

Integrity Impact

HIGH

Availability Impact

HIGH

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

AI Score

9.5

Confidence

High

EPSS

0.002

Percentile

64.8%

JSON

In Tensorflow version 2.3.0, the SparseCountSparseOutput and RaggedCountSparseOutput implementations don’t validate that the weights tensor has the same shape as the data. The check exists for DenseCountSparseOutput, where both tensors are fully specified. In the sparse and ragged count weights are still accessed in parallel with the data. But, since there is no validation, a user passing fewer weights than the values for the tensors can generate a read from outside the bounds of the heap buffer allocated for the weights. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.

CNA Affected

[
  {
    "product": "tensorflow",
    "vendor": "tensorflow",
    "versions": [
      {
        "status": "affected",
        "version": "= 2.3.0"
      }
    ]
  }
]