CVE-2020-15213: Tensorflow Denial of Service Vulnerability
MODERATECVE-2020-15213 is a MODERATE (CVSS 4.0) denial of service vulnerability (CWE-770: Allocation of Resources Without Limits or Throttling) affecting Tensorflow libraries in the PyPI ecosystem. The issue involves denial of service in tensorflow-lite. Remediation is available by upgrading affected packages to version 2.2.1, 2.3.1.
CVE-2020-15213 at a Glance
Security Impact
CVSS: 4.0
The security impact metrics below are parsed directly from the CVSS v3.1 vector string (CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:N/I:N/A:L).
How to Fix CVE-2020-15213
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.
pip install tensorflow>=2.2.1
pip install tensorflow>=2.3.1
pip install tensorflow-cpu>=2.2.1
pip install tensorflow-cpu>=2.3.1
pip install tensorflow-gpu>=2.2.1
pip install tensorflow-gpu>=2.3.1
Run the following command in your project directory to verify that all resolved dependencies use the patched release:
pip show tensorflow
How to Check if You Are Affected
To determine whether your Python environment is affected by CVE-2020-15213, inspect your installed packages using pip:
pip show tensorflow
Analyze 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 2.2.1), 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-770: Allocation of Resources Without Limits or Throttling, where the product allocates a reusable resource or group of resources on behalf of an actor without imposing any intended restrictions on the size or number of resources that can be allocated. 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
CVE-2020-15213 Affected Tensorflow Versions
6 component(s)| Product / Package | Ecosystem | Affected Versions | Fixed Version |
|---|---|---|---|
|
tensorflow
|
PyPI |
≥ 2.2.0
< 2.2.1
All 1 versions
2.2.0
|
2.2.1
|
|
tensorflow
|
PyPI |
≥ 2.3.0
< 2.3.1
All 1 versions
2.3.0
|
2.3.1
|
|
tensorflow-cpu
|
PyPI |
≥ 2.2.0
< 2.2.1
All 1 versions
2.2.0
|
2.2.1
|
|
tensorflow-cpu
|
PyPI |
≥ 2.3.0
< 2.3.1
All 1 versions
2.3.0
|
2.3.1
|
|
tensorflow-gpu
|
PyPI |
≥ 2.2.0
< 2.2.1
All 1 versions
2.2.0
|
2.2.1
|
|
tensorflow-gpu
|
PyPI |
≥ 2.3.0
< 2.3.1
All 1 versions
2.3.0
|
2.3.1
|
References
Frequently Asked Questions
What is CVE-2020-15213?
Which Tensorflow versions are affected?
What version fixes CVE-2020-15213?
Is tensorflow affected?
Is tensorflow-cpu affected?
What is the CVSS score of CVE-2020-15213?
What CWE is associated with CVE-2020-15213?
How can I check PyPI dependencies for CVE-2020-15213?
Machine-readable OSV JSON
OSV v1.5 Schema
{
"schema_version" : "1.4.0",
"id" : "GHSA-hjmq-236j-8m87",
"modified" : "2024-10-28T15:09:38Z",
"published" : "2020-09-25T18:28:53Z",
"aliases" : [ "CVE-2020-15213" ],
"summary" : "Denial of service in tensorflow-lite",
"details" : "### Impact\nIn TensorFlow Lite models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation:\nhttps://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/segment_sum.cc#L39-L44\n\n### Patches\nWe have patched the issue in 204945b and will release patch releases for all affected versions.\n\nWe recommend users to upgrade to TensorFlow 2.2.1, or 2.3.1.\n\n### Workarounds\nA potential workaround would be to add a custom `Verifier` to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps.\n\nHowever, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.\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 discovered from a variant analysis of [GHSA-p2cq-cprg-frvm](https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p2cq-cprg-frvm).",
"severity" : [ {
"type" : "CVSS_V3",
"score" : "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:N/I:N/A:L"
}, {
"type" : "CVSS_V4",
"score" : "CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:L"
} ],
"affected" : [ {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
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}, {
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} ],
"versions" : [ "2.2.0" ]
}, {
"package" : {
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"name" : "tensorflow"
},
"ranges" : [ {
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"versions" : [ "2.3.0" ]
}, {
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"versions" : [ "2.3.0" ]
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"versions" : [ "2.2.0" ]
}, {
"package" : {
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"name" : "tensorflow-gpu"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "2.3.0"
}, {
"fixed" : "2.3.1"
} ]
} ],
"versions" : [ "2.3.0" ]
} ],
"references" : [ {
"type" : "WEB",
"url" : "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hjmq-236j-8m87"
}, {
"type" : "ADVISORY",
"url" : "https://nvd.nist.gov/vuln/detail/CVE-2020-15213"
}, {
"type" : "WEB",
"url" : "https://github.com/tensorflow/tensorflow/commit/00c7ed7ce81c2126ebc17dfe7073b5c0efd5ec0a"
}, {
"type" : "WEB",
"url" : "https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a"
}, {
"type" : "WEB",
"url" : "https://github.com/tensorflow/tensorflow/commit/a4030d8ba3692c438997c27be2dd95f3d5f54827"
}, {
"type" : "WEB",
"url" : "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-293.yaml"
}, {
"type" : "WEB",
"url" : "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-328.yaml"
}, {
"type" : "WEB",
"url" : "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-136.yaml"
}, {
"type" : "PACKAGE",
"url" : "https://github.com/tensorflow/tensorflow"
}, {
"type" : "WEB",
"url" : "https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/segment_sum.cc#L39-L44"
}, {
"type" : "WEB",
"url" : "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1"
} ],
"database_specific" : {
"cwe_ids" : [ "CWE-119", "CWE-770" ],
"severity" : "MODERATE",
"github_reviewed" : true,
"github_reviewed_at" : "2020-09-25T18:22:48Z",
"nvd_published_at" : "2020-09-25T19:15:00Z"
}
}