ghsa-h2wq-prv9-2f56
Vulnerability from github
Impact
The implementation of tf.raw_ops.QuantizeAndDequantizeV4Grad
does not fully validate the input arguments. This results in a CHECK
-failure which can be used to trigger a denial of service attack:
```python import tensorflow as tf
tf.raw_ops.QuantizeAndDequantizeV4Grad( gradients=tf.constant(1, shape=[2,2], dtype=tf.float64), input=tf.constant(1, shape=[2,2], dtype=tf.float64), input_min=tf.constant([], shape=[0], dtype=tf.float64), input_max=tf.constant(-10, shape=[], dtype=tf.float64), axis=-1) ```
The code assumes input_min
and input_max
are scalars but there is no validation for this.
Patches
We have patched the issue in GitHub commit 098e7762d909bac47ce1dbabe6dfd06294cb9d58.
The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, as these are also affected and still in supported range.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
Attribution
This vulnerability has been reported by Neophytos Christou from Secure Systems Lab at Brown University.
{ "affected": [ { "package": { "ecosystem": "PyPI", "name": "tensorflow" }, "ranges": [ { "events": [ { "introduced": "0" }, { "fixed": "2.6.4" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow" }, "ranges": [ { "events": [ { "introduced": "2.7.0" }, { "fixed": "2.7.2" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow" }, "ranges": [ { "events": [ { "introduced": "2.8.0" }, { "fixed": "2.8.1" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-cpu" }, "ranges": [ { "events": [ { "introduced": "0" }, { "fixed": "2.6.4" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-cpu" }, "ranges": [ { "events": [ { "introduced": "2.7.0" }, { "fixed": "2.7.2" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-cpu" }, "ranges": [ { "events": [ { "introduced": "2.8.0" }, { "fixed": "2.8.1" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-gpu" }, "ranges": [ { "events": [ { "introduced": "0" }, { "fixed": "2.6.4" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-gpu" }, "ranges": [ { "events": [ { "introduced": "2.7.0" }, { "fixed": "2.7.2" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-gpu" }, "ranges": [ { "events": [ { "introduced": "2.8.0" }, { "fixed": "2.8.1" } ], "type": "ECOSYSTEM" } ] } ], "aliases": [ "CVE-2022-29192" ], "database_specific": { "cwe_ids": [ "CWE-20" ], "github_reviewed": true, "github_reviewed_at": "2022-05-24T22:06:26Z", "nvd_published_at": "2022-05-20T21:15:00Z", "severity": "MODERATE" }, "details": "### Impact\nThe implementation of [`tf.raw_ops.QuantizeAndDequantizeV4Grad`](https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L148-L226) does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack:\n\n```python\nimport tensorflow as tf\n\ntf.raw_ops.QuantizeAndDequantizeV4Grad(\n gradients=tf.constant(1, shape=[2,2], dtype=tf.float64),\n input=tf.constant(1, shape=[2,2], dtype=tf.float64),\n input_min=tf.constant([], shape=[0], dtype=tf.float64),\n input_max=tf.constant(-10, shape=[], dtype=tf.float64),\n axis=-1)\n```\n\nThe code assumes `input_min` and `input_max` are scalars but there is no validation for this.\n\n### Patches\nWe have patched the issue in GitHub commit [098e7762d909bac47ce1dbabe6dfd06294cb9d58](https://github.com/tensorflow/tensorflow/commit/098e7762d909bac47ce1dbabe6dfd06294cb9d58).\n \nThe fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, as these are also affected and still in supported range.\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### Attribution\nThis vulnerability has been reported by Neophytos Christou from Secure Systems Lab at Brown University.", "id": "GHSA-h2wq-prv9-2f56", "modified": "2022-05-24T22:06:26Z", "published": "2022-05-24T22:06:26Z", "references": [ { "type": "WEB", "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h2wq-prv9-2f56" }, { "type": "ADVISORY", "url": "https://nvd.nist.gov/vuln/detail/CVE-2022-29192" }, { "type": "WEB", "url": "https://github.com/tensorflow/tensorflow/commit/098e7762d909bac47ce1dbabe6dfd06294cb9d58" }, { "type": "PACKAGE", "url": "https://github.com/tensorflow/tensorflow" }, { "type": "WEB", "url": "https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L148-L226" }, { "type": "WEB", "url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.6.4" }, { "type": "WEB", "url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.7.2" }, { "type": "WEB", "url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.8.1" }, { "type": "WEB", "url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.9.0" } ], "schema_version": "1.4.0", "severity": [ { "score": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H", "type": "CVSS_V3" } ], "summary": "Missing validation crashes `QuantizeAndDequantizeV4Grad`" }
Sightings
Author | Source | Type | Date |
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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.