CVE-2022-21741 (GCVE-0-2022-21741)

Vulnerability from cvelistv5 – Published: 2022-02-03 14:27 – Updated: 2025-05-05 16:30
VLAI
Title
Division by zero in TFLite
Summary
Tensorflow is an Open Source Machine Learning Framework. ### Impact An attacker can craft a TFLite model that would trigger a division by zero in the implementation of depthwise convolutions. The parameters of the convolution can be user controlled and are also used within a division operation to determine the size of the padding that needs to be added before applying the convolution. There is no check before this division that the divisor is strictly positive. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
Severity
6.5 (Medium)
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
SSVC
Exploitation: poc Automatable: no Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-04-25 15:47 UTC
CWE
Assigner
References
Show details on NVD website
To clipboard 

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                  {
                    "technicalImpact": "partial"
                  }
                ],
                "role": "CISA Coordinator",
                "timestamp": "2025-04-25T15:47:04.297713Z",
                "version": "2.0.3"
              }
            }
          ]
        },
        "published": "2022-02-03T15:15:08.077",
        "references": [
          {
            "source": "security-advisories@github.com",
            "tags": [
              "Exploit",
              "Third Party Advisory"
            ],
            "url": "https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/lite/kernels/depthwise_conv.cc#L96"
          },
          {
            "source": "security-advisories@github.com",
            "tags": [
              "Patch",
              "Third Party Advisory"
            ],
            "url": "https://github.com/tensorflow/tensorflow/commit/e5b0eec199c2d03de54fd6a7fd9275692218e2bc"
          },
          {
            "source": "security-advisories@github.com",
            "tags": [
              "Patch",
              "Third Party Advisory"
            ],
            "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-428x-9xc2-m8mj"
          },
          {
            "source": "af854a3a-2127-422b-91ae-364da2661108",
            "tags": [
              "Exploit",
              "Third Party Advisory"
            ],
            "url": "https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/lite/kernels/depthwise_conv.cc#L96"
          },
          {
            "source": "af854a3a-2127-422b-91ae-364da2661108",
            "tags": [
              "Patch",
              "Third Party Advisory"
            ],
            "url": "https://github.com/tensorflow/tensorflow/commit/e5b0eec199c2d03de54fd6a7fd9275692218e2bc"
          },
          {
            "source": "af854a3a-2127-422b-91ae-364da2661108",
            "tags": [
              "Patch",
              "Third Party Advisory"
            ],
            "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-428x-9xc2-m8mj"
          }
        ],
        "sourceIdentifier": "security-advisories@github.com",
        "vulnStatus": "Modified",
        "weaknesses": [
          {
            "description": [
              {
                "lang": "en",
                "value": "CWE-369"
              }
            ],
            "source": "nvd@nist.gov",
            "type": "Primary"
          },
          {
            "description": [
              {
                "lang": "en",
                "value": "CWE-369"
              }
            ],
            "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
            "type": "Secondary"
          }
        ]
      }
    },
    "redhat_vex": {
      "current_release_date": "2025-05-29T01:29:54+00:00",
      "cve": "CVE-2022-21741",
      "id": "CVE-2022-21741",
      "initial_release_date": "2022-01-01T00:00:00+00:00",
      "product_status:known_not_affected": "1",
      "source": "Red Hat CSAF VEX",
      "status": "final",
      "title": "Division by zero in TFLite",
      "url": "https://security.access.redhat.com/data/csaf/v2/vex/2022/cve-2022-21741.json",
      "version": "3"
    },
    "suse_vex": {
      "aggregate_severity": "critical",
      "current_release_date": "2025-03-15T07:48:52Z",
      "cve": "CVE-2022-21741",
      "id": "CVE-2022-21741",
      "initial_release_date": "2023-02-15T03:29:00Z",
      "product_status:recommended": "2",
      "source": "SUSE CSAF VEX",
      "status": "interim",
      "title": "SUSE CVE CVE-2022-21741",
      "url": "https://ftp.suse.com/pub/projects/security/csaf-vex/cve-2022-21741.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.