CVE-2021-29584: Tensorflow CHECK-fail Due To Integer Overflow Vulnerability
LOW
Advisory Identifier:
GHSA-xvjm-fvxx-q3hv
Advisory Summary
CVE-2021-29584 is a LOW (CVSS 2.5) check-fail due to integer overflow vulnerability (CWE-190: Integer Overflow or Wraparound) affecting Tensorflow libraries in the PyPI ecosystem. The issue involves check-fail due to integer overflow. Remediation is available by upgrading affected packages to version 2.1.4, 2.2.3, 2.3.3, 2.4.2.
CVE-2021-29584 at a Glance
CVE Identifier
CVE-2021-29584
GHSA Identifier
GHSA-xvjm-fvxx-q3hv
Severity
LOW
CVSS v3.1 Base Score
2.5
CWE Classification
CWE-190: Integer Overflow or Wraparound
Affected Product
Tensorflow
Package Ecosystem
PyPI
Published Date
Last Updated
Security Impact
CVSS: 2.5
The security impact metrics below are parsed directly from the CVSS v3.1 vector string (CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L).
Attack Vector
Local (AV:L)
Attack Complexity
High (AC:H)
Privileges Required
Low (PR:L)
User Interaction
None (UI:N)
Scope
Unchanged (S:U)
Confidentiality
None (C:N)
Integrity
None (I:N)
Availability
Low (A:L)
How to Fix CVE-2021-29584
To remediate this vulnerability, upgrade all affected Tensorflow dependencies to a patched release. Both direct and transitive dependencies should be checked, as frameworks or security libraries may pull in vulnerable versions transitively.
tensorflow
fixed: 2.1.4
pip install tensorflow>=2.1.4
tensorflow
fixed: 2.2.3
pip install tensorflow>=2.2.3
tensorflow
fixed: 2.3.3
pip install tensorflow>=2.3.3
tensorflow
fixed: 2.4.2
pip install tensorflow>=2.4.2
tensorflow-cpu
fixed: 2.1.4
pip install tensorflow-cpu>=2.1.4
tensorflow-cpu
fixed: 2.2.3
pip install tensorflow-cpu>=2.2.3
tensorflow-cpu
fixed: 2.3.3
pip install tensorflow-cpu>=2.3.3
tensorflow-cpu
fixed: 2.4.2
pip install tensorflow-cpu>=2.4.2
tensorflow-gpu
fixed: 2.1.4
pip install tensorflow-gpu>=2.1.4
tensorflow-gpu
fixed: 2.2.3
pip install tensorflow-gpu>=2.2.3
tensorflow-gpu
fixed: 2.3.3
pip install tensorflow-gpu>=2.3.3
tensorflow-gpu
fixed: 2.4.2
pip install tensorflow-gpu>=2.4.2
Verify Direct and Transitive Dependencies
Run the following command in your project directory to verify that all resolved dependencies use the patched release:
pip show tensorflowHow to Check if You Are Affected
To determine whether your Python environment is affected by CVE-2021-29584, inspect your installed packages using pip:
pip show tensorflowAnalyze the terminal output to locate any version of Tensorflow in your dependency tree. If any resolved version matches the vulnerable ranges listed in the table below (for example, versions earlier than 2.1.4), your application is affected.
Note: The pip show tensorflow command checks currently installed packages in your active Python environment.
Technical Details
Vulnerability Mechanism & Technical Analysis
This vulnerability is classified under CWE-190: Integer Overflow or Wraparound, where the product performs a calculation that can produce an integer overflow or wraparound when the logic assumes that the resulting value will always be larger than the original value. this occurs when an integer value is incremented to a value that is too large to store in the associated representation. when this occurs, the value may become a very small or negative number. In Tensorflow, software state validation or input boundaries are circumvented during processing. Review the official source advisory statement below for specific affected components, classes, and reproduction details.
Source Advisory Statement
### Impact
An attacker can trigger a denial of service via a `CHECK`-fail in caused by an integer overflow in constructing a new tensor shape:
```python
import tensorflow as tf
input_layer = 2**60-1
sparse_data = tf.raw_ops.SparseSplit(
split_dim=1,
indices=[(0, 0), (0, 1), (0, 2),
(4, 3), (5, 0), (5, 1)],
values=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
shape=(input_layer, input_layer),
num_split=2,
name=None
)
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/0908c2f2397c099338b901b067f6495a5b96760b/tensorflow/core/kernels/sparse_split_op.cc#L66-L70) builds a dense shape without checking that the dimensions would not result in overflow:
```cc
sparse::SparseTensor sparse_tensor;
OP_REQUIRES_OK(context,
sparse::SparseTensor::Create(
input_indices, input_values,
TensorShape(input_shape.vec<int64>()), &sparse_tensor));
```
The [`TensorShape` constructor](https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L183-L188) uses a `CHECK` operation which triggers when [`InitDims`](https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L212-L296) returns a non-OK status.
```cc
template <class Shape>
TensorShapeBase<Shape>::TensorShapeBase(gtl::ArraySlice<int64> dim_sizes) {
set_tag(REP16);
set_data_type(DT_INVALID);
TF_CHECK_OK(InitDims(dim_sizes));
}
```
In our scenario, this occurs when adding a dimension from the argument results in overflow:
```cc
template <class Shape>
Status TensorShapeBase<Shape>::InitDims(gtl::ArraySlice<int64> dim_sizes) {
...
Status status = Status::OK();
for (int64 s : dim_sizes) {
status.Update(AddDimWithStatus(internal::SubtleMustCopy(s)));
if (!status.ok()) {
return status;
}
}
}
template <class Shape>
Status TensorShapeBase<Shape>::AddDimWithStatus(int64 size) {
...
int64 new_num_elements;
if (kIsPartial && (num_elements() < 0 || size < 0)) {
new_num_elements = -1;
} else {
new_num_elements = MultiplyWithoutOverflow(num_elements(), size);
if (TF_PREDICT_FALSE(new_num_elements < 0)) {
return errors::Internal("Encountered overflow when multiplying ",
num_elements(), " with ", size,
", result: ", new_num_elements);
}
}
...
}
```
This is a legacy implementation of the constructor and operations should use `BuildTensorShapeBase` or `AddDimWithStatus` to prevent `CHECK`-failures in the presence of overflows.
### Patches
We have patched the issue in GitHub commit [4c0ee937c0f61c4fc5f5d32d9bb4c67428012a60](https://github.com/tensorflow/tensorflow/commit/4c0ee937c0f61c4fc5f5d32d9bb4c67428012a60).
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](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.
### Attribution
This vulnerability has been reported by researchers from University of Virginia and University of California, Santa Barbara.
CVE-2021-29584 Affected Tensorflow Versions
12 component(s)
Fixed Versions:
2.1.42.2.32.3.32.4.2
| Product / Package | Ecosystem | Affected Versions | Fixed Version |
|---|---|---|---|
|
tensorflow
|
PyPI |
< 2.1.4
|
2.1.4
|
|
tensorflow
|
PyPI |
≥ 2.2.0
< 2.2.3
|
2.2.3
|
|
tensorflow
|
PyPI |
≥ 2.3.0
< 2.3.3
|
2.3.3
|
|
tensorflow
|
PyPI |
≥ 2.4.0
< 2.4.2
|
2.4.2
|
|
tensorflow-cpu
|
PyPI |
< 2.1.4
|
2.1.4
|
|
tensorflow-cpu
|
PyPI |
≥ 2.2.0
< 2.2.3
|
2.2.3
|
|
tensorflow-cpu
|
PyPI |
≥ 2.3.0
< 2.3.3
|
2.3.3
|
|
tensorflow-cpu
|
PyPI |
≥ 2.4.0
< 2.4.2
|
2.4.2
|
|
tensorflow-gpu
|
PyPI |
< 2.1.4
|
2.1.4
|
|
tensorflow-gpu
|
PyPI |
≥ 2.2.0
< 2.2.3
|
2.2.3
|
|
tensorflow-gpu
|
PyPI |
≥ 2.3.0
< 2.3.3
|
2.3.3
|
|
tensorflow-gpu
|
PyPI |
≥ 2.4.0
< 2.4.2
|
2.4.2
|
References
GitHub Advisory / Wiki
ADVISORY
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xvjm-fvxx-q3hv
NVD — CVE-2021-29584
NVD
https://nvd.nist.gov/vuln/detail/CVE-2021-29584
Security Fix Commit
FIX COMMIT
https://github.com/tensorflow/tensorflow/commit/4c0ee937c0f61c4fc5f5d32d9bb4c67428012a60
Tensorflow Source Repository
REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-512.yaml
Tensorflow Source Repository
REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-710.yaml
Tensorflow Source Repository
REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-221.yaml
Tensorflow Source Repository
REPOSITORY
https://github.com/tensorflow/tensorflow
Frequently Asked Questions
What is CVE-2021-29584?
CVE-2021-29584 is a security vulnerability in Tensorflow involving CHECK-fail due to integer overflow under CWE-190: Integer Overflow or Wraparound.
Which Tensorflow versions are affected?
Affected versions include releases prior to 2.1.4, 2.2.3, 2.3.3, 2.4.2 across affected packages: tensorflow, tensorflow-cpu, tensorflow-gpu.
What version fixes CVE-2021-29584?
Upgrading to 2.1.4, 2.2.3, 2.3.3, 2.4.2 (or the latest patched release for your respective branch) resolves the vulnerability.
Is tensorflow affected?
Yes, tensorflow is affected in releases prior to 2.1.4, 2.2.3, 2.3.3, 2.4.2. Upgrade your dependency to 2.1.4, 2.2.3, 2.3.3, 2.4.2 or later.
Is tensorflow-cpu affected?
Yes, tensorflow-cpu is affected in releases prior to 2.1.4, 2.2.3, 2.3.3, 2.4.2. Upgrade your dependency to 2.1.4, 2.2.3, 2.3.3, 2.4.2 or later.
What is the CVSS score of CVE-2021-29584?
CVE-2021-29584 has a CVSS v3.1 base score of 2.5 (LOW) with vector string CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L.
What CWE is associated with CVE-2021-29584?
CVE-2021-29584 is classified under CWE-190: Integer Overflow or Wraparound (The product performs a calculation that can
produce an integer overflow or wraparound when the logic
assumes that the resulting value will always be larger than
the original value. This occurs when an integer value is
incremented to a value that is too large to store in the
associated representation. When this occurs, the value may
become a very small or negative number.).
How can I check PyPI dependencies for CVE-2021-29584?
Run the dependency tree command: 'pip show tensorflow'. This command lists direct and transitive dependencies in your build configuration so you can verify if a vulnerable version is resolved.
Machine-readable OSV JSON
OSV v1.5 Schema
Normalized schema output
{
"schema_version" : "1.4.0",
"id" : "GHSA-xvjm-fvxx-q3hv",
"modified" : "2024-11-13T15:59:44Z",
"published" : "2021-05-21T14:26:38Z",
"aliases" : [ "CVE-2021-29584" ],
"summary" : "CHECK-fail due to integer overflow",
"details" : "### Impact\nAn attacker can trigger a denial of service via a `CHECK`-fail in caused by an integer overflow in constructing a new tensor shape:\n\n```python\nimport tensorflow as tf\n\ninput_layer = 2**60-1\nsparse_data = tf.raw_ops.SparseSplit(\n split_dim=1, \n indices=[(0, 0), (0, 1), (0, 2), \n (4, 3), (5, 0), (5, 1)],\n values=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0],\n shape=(input_layer, input_layer),\n num_split=2,\n name=None\n )\n```\n \nThis is because the [implementation](https://github.com/tensorflow/tensorflow/blob/0908c2f2397c099338b901b067f6495a5b96760b/tensorflow/core/kernels/sparse_split_op.cc#L66-L70) builds a dense shape without checking that the dimensions would not result in overflow:\n\n```cc\nsparse::SparseTensor sparse_tensor;\nOP_REQUIRES_OK(context,\n sparse::SparseTensor::Create(\n input_indices, input_values,\n TensorShape(input_shape.vec<int64>()), &sparse_tensor));\n```\n\nThe [`TensorShape` constructor](https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L183-L188) uses a `CHECK` operation which triggers when [`InitDims`](https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L212-L296) returns a non-OK status.\n \n```cc \ntemplate <class Shape>\nTensorShapeBase<Shape>::TensorShapeBase(gtl::ArraySlice<int64> dim_sizes) {\n set_tag(REP16);\n set_data_type(DT_INVALID);\n TF_CHECK_OK(InitDims(dim_sizes));\n}\n```\n\nIn our scenario, this occurs when adding a dimension from the argument results in overflow:\n\n```cc\ntemplate <class Shape>\nStatus TensorShapeBase<Shape>::InitDims(gtl::ArraySlice<int64> dim_sizes) {\n ...\n Status status = Status::OK();\n for (int64 s : dim_sizes) {\n status.Update(AddDimWithStatus(internal::SubtleMustCopy(s)));\n if (!status.ok()) {\n return status;\n }\n }\n}\n\ntemplate <class Shape>\nStatus TensorShapeBase<Shape>::AddDimWithStatus(int64 size) {\n ...\n int64 new_num_elements;\n if (kIsPartial && (num_elements() < 0 || size < 0)) {\n new_num_elements = -1;\n } else {\n new_num_elements = MultiplyWithoutOverflow(num_elements(), size);\n if (TF_PREDICT_FALSE(new_num_elements < 0)) {\n return errors::Internal(\"Encountered overflow when multiplying \",\n num_elements(), \" with \", size,\n \", result: \", new_num_elements);\n }\n }\n ...\n}\n```\n\nThis is a legacy implementation of the constructor and operations should use `BuildTensorShapeBase` or `AddDimWithStatus` to prevent `CHECK`-failures in the presence of overflows.\n\n### Patches\nWe have patched the issue in GitHub commit [4c0ee937c0f61c4fc5f5d32d9bb4c67428012a60](https://github.com/tensorflow/tensorflow/commit/4c0ee937c0f61c4fc5f5d32d9bb4c67428012a60).\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 researchers from University of Virginia and University of California, Santa Barbara.",
"severity" : [ {
"type" : "CVSS_V3",
"score" : "CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L"
}, {
"type" : "CVSS_V4",
"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"
} ],
"affected" : [ {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "0"
}, {
"fixed" : "2.1.4"
} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "2.2.0"
}, {
"fixed" : "2.2.3"
} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "2.3.0"
}, {
"fixed" : "2.3.3"
} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "2.4.0"
}, {
"fixed" : "2.4.2"
} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow-cpu"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "0"
}, {
"fixed" : "2.1.4"
} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow-cpu"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "2.2.0"
}, {
"fixed" : "2.2.3"
} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow-cpu"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "2.3.0"
}, {
"fixed" : "2.3.3"
} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow-cpu"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "2.4.0"
}, {
"fixed" : "2.4.2"
} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow-gpu"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "0"
}, {
"fixed" : "2.1.4"
} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow-gpu"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "2.2.0"
}, {
"fixed" : "2.2.3"
} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow-gpu"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "2.3.0"
}, {
"fixed" : "2.3.3"
} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow-gpu"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "2.4.0"
}, {
"fixed" : "2.4.2"
} ]
} ]
} ],
"references" : [ {
"type" : "WEB",
"url" : "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xvjm-fvxx-q3hv"
}, {
"type" : "ADVISORY",
"url" : "https://nvd.nist.gov/vuln/detail/CVE-2021-29584"
}, {
"type" : "WEB",
"url" : "https://github.com/tensorflow/tensorflow/commit/4c0ee937c0f61c4fc5f5d32d9bb4c67428012a60"
}, {
"type" : "WEB",
"url" : "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-512.yaml"
}, {
"type" : "WEB",
"url" : "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-710.yaml"
}, {
"type" : "WEB",
"url" : "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-221.yaml"
}, {
"type" : "PACKAGE",
"url" : "https://github.com/tensorflow/tensorflow"
} ],
"database_specific" : {
"cwe_ids" : [ "CWE-190" ],
"severity" : "LOW",
"github_reviewed" : true,
"github_reviewed_at" : "2021-05-18T17:23:38Z",
"nvd_published_at" : "2021-05-14T20:15:00Z"
}
}