Vulnerability-Lookup

CVE-2023-25661 (GCVE-0-2023-25661)

Vulnerability from cvelistv5 – Published: 2023-03-27 19:52 – Updated: 2025-02-19 15:26

Title

Denial of Service in TensorFlow

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.

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 2025-02-19 15:25 UTC

CWE

CWE-20 - Improper Input Validation

Assigner

GitHub_M CNA under the mitre root

CNA scorecard C 72/100 over 7613 records in the last 180 days details

References

2 references

URL	Tags
https://github.com/tensorflow/tensorflow/security…	x_refsource_CONFIRM
https://github.com/tensorflow/tensorflow/commit/9…	x_refsource_MISC

Impacted products

1 product

Vendor	Product	Version	CPE status
tensorflow	tensorflow	Affected: < 2.11.1	guessed

Show details on NVD website

JSON

To clipboard

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bit-tensorflow-2023-25661

Vulnerability from bitnami_vulndb

Published

2024-03-06 11:09

Modified

2026-09-08 08:34

Summary

Denial of Service in TensorFlow

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.

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "Bitnami",
        "name": "tensorflow",
        "purl": "pkg:bitnami/tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "2.11.1"
            }
          ],
          "type": "SEMVER"
        }
      ],
      "severity": [
        {
          "score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
          "type": "CVSS_V3"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2023-25661"
  ],
  "database_specific": {
    "cpes": [
      "cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*"
    ],
    "severity": "Medium"
  },
  "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": "BIT-tensorflow-2023-25661",
  "modified": "2026-09-08T08:34:36.952Z",
  "published": "2024-03-06T11:09:28.080Z",
  "references": [
    {
      "type": "FIX",
      "url": "https://github.com/tensorflow/tensorflow/commit/948fe6369a5711d4b4568ea9bbf6015c6dfb77e2"
    },
    {
      "type": "ADVISORY",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fxgc-95xx-grvq"
    },
    {
      "type": "WEB",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2023-25661"
    }
  ],
  "schema_version": "1.5.0",
  "summary": "Denial of Service in TensorFlow"
}

FKIE_CVE-2023-25661

Vulnerability from fkie_nvd - Published: 2023-03-27 20:15 - Updated: 2026-06-17 05:41

Severity

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

Summary

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
	google	tensorflow	*

JSON

To clipboard

{
  "affected": [
    {
      "affectedData": [
        {
          "product": "tensorflow",
          "vendor": "tensorflow",
          "versions": [
            {
              "status": "affected",
              "version": "\u003c 2.11.1"
            }
          ]
        }
      ],
      "source": "security-advisories@github.com"
    }
  ],
  "configurations": [
    {
      "nodes": [
        {
          "cpeMatch": [
            {
              "criteria": "cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*",
              "matchCriteriaId": "677029E5-422E-423C-9C8E-95A8483E51AE",
              "versionEndExcluding": "2.11.1",
              "vulnerable": true
            }
          ],
          "negate": false,
          "operator": "OR"
        }
      ]
    }
  ],
  "cveTags": [],
  "descriptions": [
    {
      "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."
    }
  ],
  "id": "CVE-2023-25661",
  "lastModified": "2026-06-17T05:41:41.160",
  "metrics": {
    "cvssMetricV31": [
      {
        "cvssData": {
          "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,
        "source": "security-advisories@github.com",
        "type": "Secondary"
      },
      {
        "cvssData": {
          "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,
        "source": "nvd@nist.gov",
        "type": "Primary"
      }
    ],
    "ssvcV203": [
      {
        "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
        "ssvcData": {
          "id": "CVE-2023-25661",
          "options": [
            {
              "exploitation": "poc"
            },
            {
              "automatable": "no"
            },
            {
              "technicalImpact": "partial"
            }
          ],
          "role": "CISA Coordinator",
          "timestamp": "2025-02-19T15:25:34.132307Z",
          "version": "2.0.3"
        }
      }
    ]
  },
  "published": "2023-03-27T20:15:09.417",
  "references": [
    {
      "source": "security-advisories@github.com",
      "tags": [
        "Patch"
      ],
      "url": "https://github.com/tensorflow/tensorflow/commit/948fe6369a5711d4b4568ea9bbf6015c6dfb77e2"
    },
    {
      "source": "security-advisories@github.com",
      "tags": [
        "Exploit",
        "Patch",
        "Vendor Advisory"
      ],
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fxgc-95xx-grvq"
    },
    {
      "source": "af854a3a-2127-422b-91ae-364da2661108",
      "tags": [
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      "url": "https://github.com/tensorflow/tensorflow/commit/948fe6369a5711d4b4568ea9bbf6015c6dfb77e2"
    },
    {
      "source": "af854a3a-2127-422b-91ae-364da2661108",
      "tags": [
        "Exploit",
        "Patch",
        "Vendor Advisory"
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      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fxgc-95xx-grvq"
    }
  ],
  "sourceIdentifier": "security-advisories@github.com",
  "vulnStatus": "Modified",
  "weaknesses": [
    {
      "description": [
        {
          "lang": "en",
          "value": "CWE-20"
        }
      ],
      "source": "security-advisories@github.com",
      "type": "Secondary"
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    {
      "description": [
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          "lang": "en",
          "value": "NVD-CWE-noinfo"
        }
      ],
      "source": "nvd@nist.gov",
      "type": "Primary"
    }
  ]
}

GHSA-FXGC-95XX-GRVQ

Vulnerability from github – Published: 2023-03-27 21:05 – Updated: 2023-09-01 15:23

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.

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.

Severity

6.5 (Medium)


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

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"
            },
            {
              "fixed": "2.11.1"
            }
          ],
          "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 - Updated: 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",
      "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": [
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                "product_data": [
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                    "product_name": "tensorflow",
                    "version": {
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                          "version_affected": "=",
                          "version_value": "\u003c 2.11.1"
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            "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": [
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            "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"
    }
  }
}

MSRC_CVE-2023-25661

Vulnerability from csaf_microsoft - Published: 2023-03-10 00:00 - Updated: 2023-10-11 00:00

Summary

Denial of Service in TensorFlow

Scope

1 vulnerability in this advisory, including CVE-2023-25661.

CVE-2023-25661

6.5 (Medium)


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

Fixed: 1 product Known affected: 1 product

https://learn.microsoft.com/en-us/azure/azure-linux/tutoria…

Open the full advisory

PYSEC-2026-1952

Vulnerability from pysec - Published: 2026-07-07 11:45 - Updated: 2026-07-07 17:25

Details

Impact

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

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.

Severity

6.5 (Medium)


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

Impacted products

Name	purl
tensorflow-cpu	pkg:pypi/tensorflow-cpu

Aliases

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu",
        "purl": "pkg:pypi/tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "2.11.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "1.15.0",
        "2.1.0",
        "2.1.1",
        "2.1.2",
        "2.1.3",
        "2.1.4",
        "2.10.0",
        "2.10.0rc0",
        "2.10.0rc1",
        "2.10.0rc2",
        "2.10.0rc3",
        "2.10.1",
        "2.11.0",
        "2.11.0rc0",
        "2.11.0rc1",
        "2.11.0rc2",
        "2.2.0",
        "2.2.1",
        "2.2.2",
        "2.2.3",
        "2.3.0",
        "2.3.1",
        "2.3.2",
        "2.3.3",
        "2.3.4",
        "2.4.0",
        "2.4.1",
        "2.4.2",
        "2.4.3",
        "2.4.4",
        "2.5.0",
        "2.5.1",
        "2.5.2",
        "2.5.3",
        "2.6.0",
        "2.6.1",
        "2.6.2",
        "2.6.3",
        "2.6.4",
        "2.6.5",
        "2.7.0",
        "2.7.1",
        "2.7.2",
        "2.7.3",
        "2.7.4",
        "2.8.0",
        "2.8.1",
        "2.8.2",
        "2.8.3",
        "2.8.4",
        "2.9.0",
        "2.9.0rc0",
        "2.9.0rc1",
        "2.9.0rc2",
        "2.9.1",
        "2.9.2",
        "2.9.3"
      ]
    }
  ],
  "aliases": [
    "CVE-2023-25661",
    "GHSA-fxgc-95xx-grvq"
  ],
  "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": "PYSEC-2026-1952",
  "modified": "2026-07-07T17:25:27.712433Z",
  "published": "2026-07-07T11:45:17.595902Z",
  "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"
    },
    {
      "type": "PACKAGE",
      "url": "https://pypi.org/project/tensorflow-cpu"
    },
    {
      "type": "ADVISORY",
      "url": "https://github.com/advisories/GHSA-fxgc-95xx-grvq"
    }
  ],
  "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"
}

PYSEC-2026-3182

Vulnerability from pysec - Published: 2026-07-13 14:20 - Updated: 2026-07-13 16:07

Details

Impact

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

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.

Severity

6.5 (Medium)


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

Impacted products

Name	purl
tensorflow	pkg:pypi/tensorflow

Aliases

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow",
        "purl": "pkg:pypi/tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "2.11.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "0.12.0",
        "0.12.1",
        "1.0.0",
        "1.0.1",
        "1.1.0",
        "1.10.0",
        "1.10.1",
        "1.11.0",
        "1.12.0",
        "1.12.2",
        "1.12.3",
        "1.13.1",
        "1.13.2",
        "1.14.0",
        "1.15.0",
        "1.15.2",
        "1.15.3",
        "1.15.4",
        "1.15.5",
        "1.2.0",
        "1.2.1",
        "1.3.0",
        "1.4.0",
        "1.4.1",
        "1.5.0",
        "1.5.1",
        "1.6.0",
        "1.7.0",
        "1.7.1",
        "1.8.0",
        "1.9.0",
        "2.0.0",
        "2.0.1",
        "2.0.2",
        "2.0.3",
        "2.0.4",
        "2.1.0",
        "2.1.1",
        "2.1.2",
        "2.1.3",
        "2.1.4",
        "2.10.0",
        "2.10.0rc0",
        "2.10.0rc1",
        "2.10.0rc2",
        "2.10.0rc3",
        "2.10.1",
        "2.11.0",
        "2.11.0rc0",
        "2.11.0rc1",
        "2.11.0rc2",
        "2.2.0",
        "2.2.1",
        "2.2.2",
        "2.2.3",
        "2.3.0",
        "2.3.1",
        "2.3.2",
        "2.3.3",
        "2.3.4",
        "2.4.0",
        "2.4.1",
        "2.4.2",
        "2.4.3",
        "2.4.4",
        "2.5.0",
        "2.5.1",
        "2.5.2",
        "2.5.3",
        "2.6.0",
        "2.6.0rc0",
        "2.6.0rc1",
        "2.6.0rc2",
        "2.6.1",
        "2.6.2",
        "2.6.3",
        "2.6.4",
        "2.6.5",
        "2.7.0",
        "2.7.0rc0",
        "2.7.0rc1",
        "2.7.1",
        "2.7.2",
        "2.7.3",
        "2.7.4",
        "2.8.0",
        "2.8.0rc0",
        "2.8.0rc1",
        "2.8.1",
        "2.8.2",
        "2.8.3",
        "2.8.4",
        "2.9.0",
        "2.9.0rc0",
        "2.9.0rc1",
        "2.9.0rc2",
        "2.9.1",
        "2.9.2",
        "2.9.3"
      ]
    }
  ],
  "aliases": [
    "CVE-2023-25661",
    "GHSA-fxgc-95xx-grvq"
  ],
  "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": "PYSEC-2026-3182",
  "modified": "2026-07-13T16:07:06.225084Z",
  "published": "2026-07-13T14:20:01.188911Z",
  "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"
    },
    {
      "type": "PACKAGE",
      "url": "https://pypi.org/project/tensorflow"
    },
    {
      "type": "ADVISORY",
      "url": "https://github.com/advisories/GHSA-fxgc-95xx-grvq"
    }
  ],
  "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"
}

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.

Vulnerability timeline

Sightings and recorded dates, grouped by day · UTC

Order JSON

Publication & reservation Sightings KEV catalogs Other timestamps

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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base model, served locally by ML-Gateway. They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.

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.

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CVE-2023-25661 (GCVE-0-2023-25661)

bit-tensorflow-2023-25661

Vulnerability from bitnami_vulndb

FKIE_CVE-2023-25661

GHSA-FXGC-95XX-GRVQ

Impact

Patches

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GSD-2023-25661

MSRC_CVE-2023-25661

PYSEC-2026-1952

Impact

Patches

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PYSEC-2026-3182

Impact

Patches

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Vulnerability timeline

Related by attack behaviour