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TensorflowApplication · Google

CVE-2022-23594

MEDIUM · 5.5 CVSS v3.1 Published 2022-02-04
Mitigation only
No fix yet — a mitigation exists. There is no fixed release. A documented workaround reduces exposure in the meantime.
See remediation →
57/100
Remediation priority · Elevated
Zero-click

Official description Straight from the sourceThe vendor's or NVD's own wording, published unedited. Authoritative, but often terse — it says what broke, rarely what to do.

NVD · unedited
Tensorflow is an Open Source Machine Learning Framework. The TFG dialect of TensorFlow (MLIR) makes several assumptions about the incoming `GraphDef` before converting it to the MLIR-based dialect. If an attacker changes the `SavedModel` format on disk to invalidate these assumptions and the `GraphDef` is then converted to MLIR-based IR then they can cause a crash in the Python interpreter. Under certain scenarios, heap OOB read/writes are possible. These issues have been discovered via fuzzing and it is possible that more weaknesses exist. We will patch them as they are discovered.

Technical summary Written by usOur analysis, written from the advisory, the CVSS vector and the affected-version data. It adds context the advisory leaves out, and never invents facts that are not in the source.

dbcve analysis · moderate confidence

The TFG dialect of TensorFlow (MLIR) makes assumptions about incoming GraphDef format before conversion. If an attacker modifies the SavedModel format on disk to invalidate these assumptions and the GraphDef is then converted to MLIR-based IR, it can cause a Python interpreter crash. Under certain conditions, heap out-of-bounds read/write access is possible.

MitigationImplement robust validation of GraphDef/SavedModel format before MLIR conversion to ensure all assumptions are met; update TensorFlow to patched versions as they become available.

Verify against the referenced sources before acting — the references below are authoritative for this CVE, this summary is not.

Affected products & versions What the vendor confirmedThe version ranges the vendor confirmed as vulnerable. If your version sits inside a range here, treat yourself as exposed until you have upgraded.

NVD · CPE data
TensorflowApplication
Affected:= 2.7.0

CVSS breakdown How the score is builtThe industry scoring standard. It rates how the flaw is reached, what it takes to exploit, and what an attacker gains — the score is derived from those, not the other way round.

From the vector
Attack vector
Local
Complexity
Low
Privileges
Low
User interaction
None
Scope
Unchanged
Confidentiality
None
Integrity
None
Availability
High

CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

Am I affected? How to checkSteps we derive from the advisory and the affected-version data, so you can decide whether this CVE reaches your setup. They are a guide, not a scan — your own configuration is the authority.

dbcve checks

Work through these to decide whether this CVE applies to you.

  1. Check installed TensorFlow version
    Run 'pip show tensorflow' or 'python -c "import tensorflow; print(tensorflow.__version__)"'
    Affected if Version is exactly 2.7.0
  2. Verify if SavedModel format is being loaded
    Search codebase or deployed application for calls to tf.saved_model.load() or tf.keras.models.load_model() that load SavedModel from disk
    Affected if Code loads SavedModel files from disk without prior validation
  3. Confirm MLIR-based conversion (TFG dialect) is in use
    Check for presence of TFExecutor, TFG dialect, or MLIR-based tf.function graph conversion in the processing pipeline. Look for use of 'tf.mlir.tf_function_to_graph' or similar MLIR utilities
    Affected if MLIR-based TFG dialect conversion is performed on untrusted GraphDef/SavedModel input
  4. Inspect for untrusted or externally sourced SavedModel files
    Audit filesystem locations where SavedModel directories may be loaded from (user uploads, model repositories, remote URLs). Check file permissions and integrity
    Affected if SavedModel files from untrusted sources are loaded and converted without validation

User is affected if running TensorFlow 2.7.0 and loading untrusted or modified SavedModel files through MLIR-based conversion (TFG dialect).

Generated from the published advisory. Verify against your own configuration.

Check your environment

Paste your version and any relevant configuration and it will be compared against the affected criteria above. Do not include secrets or credentials.

AI-assisted, checked against the advisory. Informational, not a guarantee.

Remediation Closing itWhat it takes to close this. Where a vendor fix exists we point at it; where none exists we say so plainly, and can build one. Effort estimates are scoped from the advisory, not from your codebase.

dbcve · scoped
Mitigation available No clean upgrade yet — mitigate in the meantime
Mitigation

Implement robust validation of GraphDef/SavedModel format before MLIR conversion to ensure all assumptions are met; update TensorFlow to patched versions as they become available.

Recommended fix High confidence

TensorFlow 2.7.1 or later (2.8.x, 2.9.x, 2.10.x, 2.11.x, 2.12.x, 2.13.x, 2.14.x, 2.15.x all contain the fix)

  1. Upgrade TensorFlow from version 2.7.0 to version 2.7.1 or later using pip: pip install --upgrade tensorflow==2.7.1
  2. Verify the upgrade was successful by running: python -c 'import tensorflow as tf; print(tf.__version__)'
  3. Test that SavedModel loading still works correctly in your application
  4. If you cannot upgrade immediately, avoid loading untrusted SavedModel files from untrusted sources until you can patch
Caveat Minor point release upgrade; minimal risk of breaking changes. TensorFlow minor releases generally maintain backward compatibility.

Generated from the published advisory — verify against the referenced sources before acting.

Fix this in Tensorflow Scoped from the published advisory
  • Consultation3.0 h
  • Implementation12.0 h
  • Testing6.0 h
  • Review / QA3.0 h
24.0 hours of engineering $4,200
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References Go to the primary sourcePrimary sources — vendor advisories, patches and trackers. Where our summary and a reference disagree, the reference wins.

Primary sources

Practitioner notes

Contributed

Peer-ranked notes from engineers who’ve handled CVE-2022-23594 in production — separate from our analysis above.

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  • Version or environment caveats, and links to real fixes
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