CVE-2020-15202 Integer truncation in Shard API usage

2020-09-2518:46:15

CWE-197

CWE-754

GitHub_M

www.cve.org

9 High

CVSS3

Attack Vector

NETWORK

Attack Complexity

HIGH

Privileges Required

NONE

User Interaction

NONE

Scope

CHANGED

Confidentiality Impact

HIGH

Integrity Impact

HIGH

Availability Impact

HIGH

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

8.9 High

AI Score

Confidence

High

0.003 Low

EPSS

Percentile

67.8%

JSON

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the Shard API in TensorFlow expects the last argument to be a function taking two int64 (i.e., long long) arguments. However, there are several places in TensorFlow where a lambda taking int or int32 arguments is being used. In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption. The issue is patched in commits 27b417360cbd671ef55915e4bb6bb06af8b8a832 and ca8c013b5e97b1373b3bb1c97ea655e69f31a575, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

CNA Affected

[
  {
    "product": "tensorflow",
    "vendor": "tensorflow",
    "versions": [
      {
        "status": "affected",
        "version": "< 1.15.4"
      },
      {
        "status": "affected",
        "version": ">= 2.0.0, < 2.0.3"
      },
      {
        "status": "affected",
        "version": ">= 2.1.0, < 2.1.2"
      },
      {
        "status": "affected",
        "version": ">= 2.2.0, < 2.2.1"
      },
      {
        "status": "affected",
        "version": ">= 2.3.0, < 2.3.1"
      }
    ]
  }
]