CVE-2020-15193 (GCVE-0-2020-15193)

Vulnerability from cvelistv5 – Published: 2020-09-25 18:40 – Updated: 2024-08-04 13:08
VLAI
Title
Memory corruption in Tensorflow
Summary
In Tensorflow before versions 2.2.1 and 2.3.1, the implementation of `dlpack.to_dlpack` can be made to use uninitialized memory resulting in further memory corruption. This is because the pybind11 glue code assumes that the argument is a tensor. However, there is nothing stopping users from passing in a Python object instead of a tensor. The uninitialized memory address is due to a `reinterpret_cast` Since the `PyObject` is a Python object, not a TensorFlow Tensor, the cast to `EagerTensor` fails. The issue is patched in commit 22e07fb204386768e5bcbea563641ea11f96ceb8 and is released in TensorFlow versions 2.2.1, or 2.3.1.
Severity
7.1 (High)
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:L
CWE
  • CWE-908 - {"CWE-908":"Use of Uninitialized Resource"}
Assigner
References
Impacted products
Vendor Product Version CPE status
tensorflow tensorflow Affected: = 2.2.0
Affected: = 2.3.0
guessed Create a notification for this product.
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            "source": "security-advisories@github.com",
            "tags": [
              "Third Party Advisory"
            ],
            "url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1"
          },
          {
            "source": "security-advisories@github.com",
            "tags": [
              "Exploit",
              "Third Party Advisory"
            ],
            "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rjjg-hgv6-h69v"
          },
          {
            "source": "af854a3a-2127-422b-91ae-364da2661108",
            "tags": [
              "Mailing List",
              "Third Party Advisory"
            ],
            "url": "http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html"
          },
          {
            "source": "af854a3a-2127-422b-91ae-364da2661108",
            "tags": [
              "Patch",
              "Third Party Advisory"
            ],
            "url": "https://github.com/tensorflow/tensorflow/commit/22e07fb204386768e5bcbea563641ea11f96ceb8"
          },
          {
            "source": "af854a3a-2127-422b-91ae-364da2661108",
            "tags": [
              "Third Party Advisory"
            ],
            "url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1"
          },
          {
            "source": "af854a3a-2127-422b-91ae-364da2661108",
            "tags": [
              "Exploit",
              "Third Party Advisory"
            ],
            "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rjjg-hgv6-h69v"
          }
        ],
        "sourceIdentifier": "security-advisories@github.com",
        "vulnStatus": "Modified",
        "weaknesses": [
          {
            "description": [
              {
                "lang": "en",
                "value": "CWE-908"
              }
            ],
            "source": "security-advisories@github.com",
            "type": "Secondary"
          },
          {
            "description": [
              {
                "lang": "en",
                "value": "CWE-908"
              }
            ],
            "source": "nvd@nist.gov",
            "type": "Primary"
          }
        ]
      }
    },
    "suse_vex": {
      "aggregate_severity": "moderate",
      "current_release_date": "2025-03-15T09:51:50Z",
      "cve": "CVE-2020-15193",
      "id": "CVE-2020-15193",
      "initial_release_date": "2023-02-15T03:56:59Z",
      "product_status:recommended": "22",
      "source": "SUSE CSAF VEX",
      "status": "interim",
      "title": "SUSE CVE CVE-2020-15193",
      "url": "https://ftp.suse.com/pub/projects/security/csaf-vex/cve-2020-15193.json",
      "version": "6"
    }
  }
}



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Tags
Taxonomy of the tags.





Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.

Sightings

Author Source Type Date Other

Nomenclature

  • Seen: The vulnerability was mentioned, discussed, or observed by the user.
  • Confirmed: The vulnerability has been validated from an analyst's perspective.
  • Published Proof of Concept: A public proof of concept is available for this vulnerability.
  • Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
  • Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
  • Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
  • Not confirmed: The user expressed doubt about the validity of the vulnerability.
  • Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.



Related by attack behaviour

Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.