gsd-2023-25661
Vulnerability from gsd
Modified
2023-12-13 01:20
Details
TensorFlow is an Open Source Machine Learning Framework. In versions prior to 2.11.1 a malicious invalid input crashes a tensorflow model (Check Failed) and can be used to trigger a denial of service attack. A proof of concept can be constructed with the `Convolution3DTranspose` function. This Convolution3DTranspose layer is a very common API in modern neural networks. The ML models containing such vulnerable components could be deployed in ML applications or as cloud services. This failure could be potentially used to trigger a denial of service attack on ML cloud services. An attacker must have privilege to provide input to a `Convolution3DTranspose` call. This issue has been patched and users are advised to upgrade to version 2.11.1. There are no known workarounds for this vulnerability.
Aliases
Aliases
CVE-2023-25661


To clipboard 

{
  "GSD": {
    "alias": "CVE-2023-25661",
    "id": "GSD-2023-25661"
  },
  "gsd": {
    "metadata": {
      "exploitCode": "unknown",
      "remediation": "unknown",
      "reportConfidence": "confirmed",
      "type": "vulnerability"
    },
    "osvSchema": {
      "aliases": [
        "CVE-2023-25661"
      ],
      "details": "TensorFlow is an Open Source Machine Learning Framework. In versions prior to 2.11.1 a malicious invalid input crashes a tensorflow model (Check Failed) and can be used to trigger a denial of service attack. A proof of concept can be constructed with the `Convolution3DTranspose` function. This Convolution3DTranspose layer is a very common API in modern neural networks. The ML models containing such vulnerable components could be deployed in ML applications or as cloud services. This failure could be potentially used to trigger a denial of service attack on ML cloud services. An attacker must have privilege to provide input to a `Convolution3DTranspose` call. This issue has been patched and users are advised to upgrade to version 2.11.1. There are no known workarounds for this vulnerability.",
      "id": "GSD-2023-25661",
      "modified": "2023-12-13T01:20:39.871763Z",
      "schema_version": "1.4.0"
    }
  },
  "namespaces": {
    "cve.org": {
      "CVE_data_meta": {
        "ASSIGNER": "security-advisories@github.com",
        "ID": "CVE-2023-25661",
        "STATE": "PUBLIC"
      },
      "affects": {
        "vendor": {
          "vendor_data": [
            {
              "product": {
                "product_data": [
                  {
                    "product_name": "tensorflow",
                    "version": {
                      "version_data": [
                        {
                          "version_affected": "=",
                          "version_value": "\u003c 2.11.1"
                        }
                      ]
                    }
                  }
                ]
              },
              "vendor_name": "tensorflow"
            }
          ]
        }
      },
      "data_format": "MITRE",
      "data_type": "CVE",
      "data_version": "4.0",
      "description": {
        "description_data": [
          {
            "lang": "eng",
            "value": "TensorFlow is an Open Source Machine Learning Framework. In versions prior to 2.11.1 a malicious invalid input crashes a tensorflow model (Check Failed) and can be used to trigger a denial of service attack. A proof of concept can be constructed with the `Convolution3DTranspose` function. This Convolution3DTranspose layer is a very common API in modern neural networks. The ML models containing such vulnerable components could be deployed in ML applications or as cloud services. This failure could be potentially used to trigger a denial of service attack on ML cloud services. An attacker must have privilege to provide input to a `Convolution3DTranspose` call. This issue has been patched and users are advised to upgrade to version 2.11.1. There are no known workarounds for this vulnerability."
          }
        ]
      },
      "impact": {
        "cvss": [
          {
            "attackComplexity": "LOW",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 6.5,
            "baseSeverity": "MEDIUM",
            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "privilegesRequired": "LOW",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
            "version": "3.1"
          }
        ]
      },
      "problemtype": {
        "problemtype_data": [
          {
            "description": [
              {
                "cweId": "CWE-20",
                "lang": "eng",
                "value": "CWE-20: Improper Input Validation"
              }
            ]
          }
        ]
      },
      "references": {
        "reference_data": [
          {
            "name": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fxgc-95xx-grvq",
            "refsource": "MISC",
            "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fxgc-95xx-grvq"
          },
          {
            "name": "https://github.com/tensorflow/tensorflow/commit/948fe6369a5711d4b4568ea9bbf6015c6dfb77e2",
            "refsource": "MISC",
            "url": "https://github.com/tensorflow/tensorflow/commit/948fe6369a5711d4b4568ea9bbf6015c6dfb77e2"
          }
        ]
      },
      "source": {
        "advisory": "GHSA-fxgc-95xx-grvq",
        "discovery": "UNKNOWN"
      }
    },
    "gitlab.com": {
      "advisories": [
        {
          "affected_range": "\u003c2.11.1",
          "affected_versions": "All versions before 2.11.1",
          "cwe_ids": [
            "CWE-1035",
            "CWE-937"
          ],
          "date": "2023-03-27",
          "description": "### Impact\nA malicious invalid input crashes a tensorflow model (Check Failed) and can be used to trigger a denial of service attack.\nTo minimize the bug, we built a simple single-layer TensorFlow model containing a Convolution3DTranspose layer, which works well with expected inputs and can be deployed in real-world systems. However, if we call the model with a malicious input which has a zero dimension, it gives Check Failed failure and crashes.\n```python\nimport tensorflow as tf\n\nclass MyModel(tf.keras.Model):\n  def __init__(self):\n    super().__init__()\n    self.conv = tf.keras.layers.Convolution3DTranspose(2, [3,3,3], padding=\"same\")\n    \n  def call(self, input):\n    return self.conv(input)\nmodel = MyModel() # Defines a valid model.\n\nx = tf.random.uniform([1, 32, 32, 32, 3], minval=0, maxval=0, dtype=tf.float32) # This is a valid input.\noutput = model.predict(x)\nprint(output.shape) # (1, 32, 32, 32, 2)\n\nx = tf.random.uniform([1, 32, 32, 0, 3], dtype=tf.float32) # This is an invalid input.\noutput = model(x) # crash\n```\nThis Convolution3DTranspose layer is a very common API in modern neural networks. The ML models containing such vulnerable components could be deployed in ML applications or as cloud services. This failure could be potentially used to trigger a denial of service attack on ML cloud services.\n\n### Patches\nWe have patched the issue in\n - GitHub commit [948fe6369a5711d4b4568ea9bbf6015c6dfb77e2](https://github.com/tensorflow/tensorflow/commit/948fe6369a5711d4b4568ea9bbf6015c6dfb77e2)\n - GitHub commit [85db5d07db54b853484bfd358c3894d948c36baf](https://github.com/keras-team/keras/commit/85db5d07db54b853484bfd358c3894d948c36baf). \n\nThe fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1\n\n\n### For more information\nPlease consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.\n\n\n",
          "fixed_versions": [
            "2.11.1"
          ],
          "identifier": "CVE-2023-25661",
          "identifiers": [
            "GHSA-fxgc-95xx-grvq",
            "CVE-2023-25661"
          ],
          "not_impacted": "All versions starting from 2.11.1",
          "package_slug": "pypi/tensorflow-cpu",
          "pubdate": "2023-03-27",
          "solution": "Upgrade to version 2.11.1 or above.",
          "title": "TensorFlow Denial of Service vulnerability",
          "urls": [
            "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fxgc-95xx-grvq",
            "https://github.com/keras-team/keras/commit/85db5d07db54b853484bfd358c3894d948c36baf",
            "https://github.com/tensorflow/tensorflow/commit/948fe6369a5711d4b4568ea9bbf6015c6dfb77e2",
            "https://nvd.nist.gov/vuln/detail/CVE-2023-25661",
            "https://github.com/advisories/GHSA-fxgc-95xx-grvq"
          ],
          "uuid": "8942bba3-1ab4-40a7-9ae0-86b42f59aa46"
        },
        {
          "affected_range": "\u003c2.11.1",
          "affected_versions": "All versions before 2.11.1",
          "cvss_v3": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
          "cwe_ids": [
            "CWE-1035",
            "CWE-937"
          ],
          "date": "2023-04-03",
          "description": "### Impact\nA malicious invalid input crashes a tensorflow model (Check Failed) and can be used to trigger a denial of service attack.\nTo minimize the bug, we built a simple single-layer TensorFlow model containing a Convolution3DTranspose layer, which works well with expected inputs and can be deployed in real-world systems. However, if we call the model with a malicious input which has a zero dimension, it gives Check Failed failure and crashes.\n```python\nimport tensorflow as tf\n\nclass MyModel(tf.keras.Model):\n def __init__(self):\n  super().__init__()\n  self.conv = tf.keras.layers.Convolution3DTranspose(2, [3,3,3], padding=\"same\")\n  \n def call(self, input):\n  return self.conv(input)\nmodel = MyModel() # Defines a valid model.\n\nx = tf.random.uniform([1, 32, 32, 32, 3], minval=0, maxval=0, dtype=tf.float32) # This is a valid input.\noutput = model.predict(x)\nprint(output.shape) # (1, 32, 32, 32, 2)\n\nx = tf.random.uniform([1, 32, 32, 0, 3], dtype=tf.float32) # This is an invalid input.\noutput = model(x) # crash\n```\nThis Convolution3DTranspose layer is a very common API in modern neural networks. The ML models containing such vulnerable components could be deployed in ML applications or as cloud services. This failure could be potentially used to trigger a denial of service attack on ML cloud services.\n\n### Patches\nWe have patched the issue in\n- GitHub commit [948fe6369a5711d4b4568ea9bbf6015c6dfb77e2](https://github.com/tensorflow/tensorflow/commit/948fe6369a5711d4b4568ea9bbf6015c6dfb77e2)\n - GitHub commit [85db5d07db54b853484bfd358c3894d948c36baf](https://github.com/keras-team/keras/commit/85db5d07db54b853484bfd358c3894d948c36baf). \n\nThe fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1\n\n\n ### For more information\nPlease consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.\n\n\n",
          "fixed_versions": [
            "2.11.1"
          ],
          "identifier": "CVE-2023-25661",
          "identifiers": [
            "CVE-2023-25661",
            "GHSA-fxgc-95xx-grvq"
          ],
          "not_impacted": "All versions starting from 2.11.1",
          "package_slug": "pypi/tensorflow",
          "pubdate": "2023-03-27",
          "solution": "Upgrade to version 2.11.1 or above.",
          "title": "TensorFlow Denial of Service vulnerability",
          "urls": [
            "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fxgc-95xx-grvq",
            "https://github.com/keras-team/keras/commit/85db5d07db54b853484bfd358c3894d948c36baf",
            "https://github.com/tensorflow/tensorflow/commit/948fe6369a5711d4b4568ea9bbf6015c6dfb77e2",
            "https://nvd.nist.gov/vuln/detail/CVE-2023-25661",
            "https://github.com/advisories/GHSA-fxgc-95xx-grvq"
          ],
          "uuid": "1c693def-8b1f-4396-aee7-d7861e97e1fa"
        }
      ]
    },
    "nvd.nist.gov": {
      "configurations": {
        "CVE_data_version": "4.0",
        "nodes": [
          {
            "children": [],
            "cpe_match": [
              {
                "cpe23Uri": "cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*",
                "cpe_name": [],
                "versionEndExcluding": "2.11.1",
                "vulnerable": true
              }
            ],
            "operator": "OR"
          }
        ]
      },
      "cve": {
        "CVE_data_meta": {
          "ASSIGNER": "security-advisories@github.com",
          "ID": "CVE-2023-25661"
        },
        "data_format": "MITRE",
        "data_type": "CVE",
        "data_version": "4.0",
        "description": {
          "description_data": [
            {
              "lang": "en",
              "value": "TensorFlow is an Open Source Machine Learning Framework. In versions prior to 2.11.1 a malicious invalid input crashes a tensorflow model (Check Failed) and can be used to trigger a denial of service attack. A proof of concept can be constructed with the `Convolution3DTranspose` function. This Convolution3DTranspose layer is a very common API in modern neural networks. The ML models containing such vulnerable components could be deployed in ML applications or as cloud services. This failure could be potentially used to trigger a denial of service attack on ML cloud services. An attacker must have privilege to provide input to a `Convolution3DTranspose` call. This issue has been patched and users are advised to upgrade to version 2.11.1. There are no known workarounds for this vulnerability."
            }
          ]
        },
        "problemtype": {
          "problemtype_data": [
            {
              "description": [
                {
                  "lang": "en",
                  "value": "NVD-CWE-noinfo"
                }
              ]
            }
          ]
        },
        "references": {
          "reference_data": [
            {
              "name": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fxgc-95xx-grvq",
              "refsource": "MISC",
              "tags": [
                "Exploit",
                "Patch",
                "Vendor Advisory"
              ],
              "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fxgc-95xx-grvq"
            },
            {
              "name": "https://github.com/tensorflow/tensorflow/commit/948fe6369a5711d4b4568ea9bbf6015c6dfb77e2",
              "refsource": "MISC",
              "tags": [
                "Patch"
              ],
              "url": "https://github.com/tensorflow/tensorflow/commit/948fe6369a5711d4b4568ea9bbf6015c6dfb77e2"
            }
          ]
        }
      },
      "impact": {
        "baseMetricV3": {
          "cvssV3": {
            "attackComplexity": "LOW",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 6.5,
            "baseSeverity": "MEDIUM",
            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "privilegesRequired": "LOW",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
            "version": "3.1"
          },
          "exploitabilityScore": 2.8,
          "impactScore": 3.6
        }
      },
      "lastModifiedDate": "2023-04-03T16:19Z",
      "publishedDate": "2023-03-27T20:15Z"
    }
  }
}


Log in or create an account to share your comment.




Tags
Taxonomy of the tags.




Sightings

Author Source Type Date

Nomenclature

  • Seen: The vulnerability was mentioned, discussed, or seen somewhere by the user.
  • Confirmed: The vulnerability is confirmed from an analyst perspective.
  • Exploited: This vulnerability was exploited and seen by the user reporting the sighting.
  • Patched: This vulnerability was successfully patched by the user reporting the sighting.
  • Not exploited: This vulnerability was not exploited or seen by the user reporting the sighting.
  • Not confirmed: The user expresses doubt about the veracity of the vulnerability.
  • Not patched: This vulnerability was not successfully patched by the user reporting the sighting.