GHSA-6GMV-PJP9-P8W8
Vulnerability from github – Published: 2022-02-09 18:29 – Updated: 2024-11-13 22:10
VLAI?
Summary
Out of bounds read in Tensorflow
Details
Impact
The implementation of shape inference for ReverseSequence does not fully validate the value of batch_dim and can result in a heap OOB read:
import tensorflow as tf
@tf.function
def test():
y = tf.raw_ops.ReverseSequence(
input = ['aaa','bbb'],
seq_lengths = [1,1,1],
seq_dim = -10,
batch_dim = -10 )
return y
test()
There is a check to make sure the value of batch_dim does not go over the rank of the input, but there is no check for negative values:
const int32_t input_rank = c->Rank(input);
if (batch_dim >= input_rank) {
return errors::InvalidArgument(
"batch_dim must be < input rank: ", batch_dim, " vs. ", input_rank);
}
// ...
DimensionHandle batch_dim_dim = c->Dim(input, batch_dim);
Negative dimensions are allowed in some cases to mimic Python's negative indexing (i.e., indexing from the end of the array), however if the value is too negative then the implementation of Dim would access elements before the start of an array:
DimensionHandle Dim(ShapeHandle s, int64_t idx) {
if (!s.Handle() || s->rank_ == kUnknownRank) {
return UnknownDim();
}
return DimKnownRank(s, idx);
}
·
static DimensionHandle DimKnownRank(ShapeHandle s, int64_t idx) {
CHECK_NE(s->rank_, kUnknownRank);
if (idx < 0) {
return s->dims_[s->dims_.size() + idx];
}
return s->dims_[idx];
}
Patches
We have patched the issue in GitHub commit 37c01fb5e25c3d80213060460196406c43d31995.
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 Yu Tian of Qihoo 360 AIVul Team.
Severity ?
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.5.3"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "2.6.0"
},
{
"fixed": "2.6.3"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "2.7.0"
},
{
"fixed": "2.7.1"
}
],
"type": "ECOSYSTEM"
}
],
"versions": [
"2.7.0"
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-cpu"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.5.3"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-cpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.6.0"
},
{
"fixed": "2.6.3"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-cpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.7.0"
},
{
"fixed": "2.7.1"
}
],
"type": "ECOSYSTEM"
}
],
"versions": [
"2.7.0"
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.5.3"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.6.0"
},
{
"fixed": "2.6.3"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.7.0"
},
{
"fixed": "2.7.1"
}
],
"type": "ECOSYSTEM"
}
],
"versions": [
"2.7.0"
]
}
],
"aliases": [
"CVE-2022-21728"
],
"database_specific": {
"cwe_ids": [
"CWE-125"
],
"github_reviewed": true,
"github_reviewed_at": "2022-02-03T18:29:10Z",
"nvd_published_at": "2022-02-03T11:15:00Z",
"severity": "HIGH"
},
"details": "### Impact \nThe [implementation of shape inference for `ReverseSequence`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/ops/array_ops.cc#L1636-L1671) does not fully validate the value of `batch_dim` and can result in a heap OOB read:\n\n```python\nimport tensorflow as tf\n\n@tf.function\ndef test():\n y = tf.raw_ops.ReverseSequence(\n input = [\u0027aaa\u0027,\u0027bbb\u0027],\n seq_lengths = [1,1,1],\n seq_dim = -10,\n batch_dim = -10 )\n return y\n \ntest()\n```\n\nThere is a check to make sure the value of `batch_dim` does not go over the rank of the input, but there is no check for negative values:\n\n```cc\n const int32_t input_rank = c-\u003eRank(input);\n if (batch_dim \u003e= input_rank) {\n return errors::InvalidArgument( \n \"batch_dim must be \u003c input rank: \", batch_dim, \" vs. \", input_rank);\n }\n // ...\n \n DimensionHandle batch_dim_dim = c-\u003eDim(input, batch_dim);\n``` \n \nNegative dimensions are allowed in some cases to mimic Python\u0027s negative indexing (i.e., indexing from the end of the array), however if the value is too negative then [the implementation of `Dim`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/framework/shape_inference.h#L415-L428) would access elements before the start of an array:\n\n```cc\n DimensionHandle Dim(ShapeHandle s, int64_t idx) {\n if (!s.Handle() || s-\u003erank_ == kUnknownRank) {\n return UnknownDim();\n }\n return DimKnownRank(s, idx);\n } \n\u00b7\n static DimensionHandle DimKnownRank(ShapeHandle s, int64_t idx) {\n CHECK_NE(s-\u003erank_, kUnknownRank);\n if (idx \u003c 0) {\n return s-\u003edims_[s-\u003edims_.size() + idx];\n }\n return s-\u003edims_[idx];\n }\n```\n\n### Patches\nWe have patched the issue in GitHub commit [37c01fb5e25c3d80213060460196406c43d31995](https://github.com/tensorflow/tensorflow/commit/37c01fb5e25c3d80213060460196406c43d31995).\n\nThe fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 Yu Tian of Qihoo 360 AIVul Team.",
"id": "GHSA-6gmv-pjp9-p8w8",
"modified": "2024-11-13T22:10:33Z",
"published": "2022-02-09T18:29:24Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6gmv-pjp9-p8w8"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2022-21728"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/37c01fb5e25c3d80213060460196406c43d31995"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2022-52.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2022-107.yaml"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/framework/shape_inference.h#L415-L428"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/ops/array_ops.cc#L1636-L1671"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:H",
"type": "CVSS_V3"
},
{
"score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:N/VA:H/SC:N/SI:N/SA:N",
"type": "CVSS_V4"
}
],
"summary": "Out of bounds read in Tensorflow"
}
Loading…
Loading…
Sightings
| Author | Source | Type | Date |
|---|
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.
Loading…
Loading…