cvelistv5 - cve-2023-25661

cve-2023-25661

Vulnerability from cvelistv5

Published

2023-03-27 19:52

Modified

2024-08-02 11:25

Severity ?

6.5 (Medium) - CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

Summary

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.

References

URL	Tags
security-advisories@github.com	https://github.com/tensorflow/tensorflow/commit/948fe6369a5711d4b4568ea9bbf6015c6dfb77e2	Patch
security-advisories@github.com	https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fxgc-95xx-grvq	Exploit, Patch, Vendor Advisory
af854a3a-2127-422b-91ae-364da2661108	https://github.com/tensorflow/tensorflow/commit/948fe6369a5711d4b4568ea9bbf6015c6dfb77e2	Patch
af854a3a-2127-422b-91ae-364da2661108	https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fxgc-95xx-grvq	Exploit, Patch, Vendor Advisory

Impacted products

Vendor

Product

Version

▼

tensorflow

Version: < 2.11.1

Show details on NVD website

JSON

To clipboard

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}

ghsa-fxgc-95xx-grvq

Vulnerability from github

Published

2023-03-27 21:05

Modified

2023-09-01 15:23

Severity ?

6.5 (Medium) - CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

Summary

TensorFlow Denial of Service vulnerability

Details

Impact

A malicious invalid input crashes a tensorflow model (Check Failed) and can be used to trigger a denial of service attack. To 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. ```python import tensorflow as tf

class MyModel(tf.keras.Model): def init(self): super().init() self.conv = tf.keras.layers.Convolution3DTranspose(2, [3,3,3], padding="same")

def call(self, input):
    return self.conv(input)

model = MyModel() # Defines a valid model.

x = tf.random.uniform([1, 32, 32, 32, 3], minval=0, maxval=0, dtype=tf.float32) # This is a valid input. output = model.predict(x) print(output.shape) # (1, 32, 32, 32, 2)

x = tf.random.uniform([1, 32, 32, 0, 3], dtype=tf.float32) # This is an invalid input. output = model(x) # crash ``` 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.

Patches

We have patched the issue in - GitHub commit 948fe6369a5711d4b4568ea9bbf6015c6dfb77e2 - GitHub commit 85db5d07db54b853484bfd358c3894d948c36baf.

The fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Show details on source website

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "2.11.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
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              "fixed": "2.11.1"
            }
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          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2023-25661"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-20"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2023-03-27T21:05:10Z",
    "nvd_published_at": "2023-03-27T20:15:00Z",
    "severity": "MODERATE"
  },
  "details": "### 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",
  "id": "GHSA-fxgc-95xx-grvq",
  "modified": "2023-09-01T15:23:50Z",
  "published": "2023-03-27T21:05:10Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fxgc-95xx-grvq"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2023-25661"
    },
    {
      "type": "WEB",
      "url": "https://github.com/keras-team/keras/commit/85db5d07db54b853484bfd358c3894d948c36baf"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/commit/948fe6369a5711d4b4568ea9bbf6015c6dfb77e2"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/tensorflow/tensorflow"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
      "type": "CVSS_V3"
    }
  ],
  "summary": "TensorFlow Denial of Service vulnerability"
}

gsd-2023-25661

Vulnerability from gsd

Modified

2023-12-13 01:20

Details

Aliases

CVE-2023-25661

Aliases

CVE-2023-25661

JSON

To clipboard

{
  "GSD": {
    "alias": "CVE-2023-25661",
    "id": "GSD-2023-25661"
  },
  "gsd": {
    "metadata": {
      "exploitCode": "unknown",
      "remediation": "unknown",
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    "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"
    }
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        "ID": "CVE-2023-25661",
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          }
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      "source": {
        "advisory": "GHSA-fxgc-95xx-grvq",
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      }
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    "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": [
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            "https://github.com/keras-team/keras/commit/85db5d07db54b853484bfd358c3894d948c36baf",
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          "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"
    }
  }
}

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.

Action not permitted

cve-2023-25661

Vulnerability from cvelistv5

ghsa-fxgc-95xx-grvq

Vulnerability from github

Impact

Patches

For more information

gsd-2023-25661

Vulnerability from gsd

Tags

Sightings

Nomenclature