CVE-2022-36014
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 · uneditedTensorFlow is an open source platform for machine learning. When `mlir::tfg::TFOp::nameAttr` receives null type list attributes, it crashes. We have patched the issue in GitHub commits 3a754740d5414e362512ee981eefba41561a63a6 and a0f0b9a21c9270930457095092f558fbad4c03e5. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.
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 analysisThe code follows a pointer that is null, crashing the process. An attacker who can reliably trigger it turns the crash into a denial of service. The fix is checking for null before use and handling the failure path gracefully.
General guidance for the null pointer dereference class — the official description and references above are authoritative for this specific CVE. Want a bespoke review and a reviewed fix? Ask our team →
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< 2.7.2>= 2.8.0, < 2.8.1>= 2.9.0, < 2.9.1= 2.10CVSS 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
- Network
- Complexity
- Low
- Privileges
- None
- User interaction
- None
- Scope
- Unchanged
- Confidentiality
- None
- Integrity
- None
- Availability
- High
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
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 · scoped2.7.22.8.12.9.1
Upgrade to TensorFlow 2.7.2, 2.8.1, 2.9.1, or 2.10.1 (depending on your current major version)
- Identify the current TensorFlow version in use using 'pip show tensorflow' or your dependency management system
- For TensorFlow < 2.7.2: upgrade to version 2.7.2 or later within the 2.7.x series
- For TensorFlow >= 2.8.0 and < 2.8.1: upgrade to version 2.8.1
- For TensorFlow >= 2.9.0 and < 2.9.1: upgrade to version 2.9.1
- For TensorFlow 2.10.0: upgrade to the latest 2.10.x release when available, or wait for 2.10.1 which will contain the fix
- After upgrading, verify the installation with 'pip show tensorflow' and test that MLIR operations with type attributes work correctly
Generated from the published advisory — verify against the referenced sources before acting.
Scan for this in your stack
Free · runs locallyCheck whether your project pulls in CVE-2022-36014 — or any other known-vulnerable package — straight from your lock files. Free and open source; it runs locally and uploads nothing.
References Go to the primary sourcePrimary sources — vendor advisories, patches and trackers. Where our summary and a reference disagree, the reference wins.
Primary sourcesPractitioner notes
ContributedPeer-ranked notes from engineers who’ve handled CVE-2022-36014 in production — separate from our analysis above.
The advisory tells you what broke. It rarely tells you what actually worked. If you’ve dealt with this one, that detail is what the next engineer is searching for.
- The version that genuinely resolved it — not the one the vendor claimed
- A config change or rule that shut the vector down
- A gotcha in the upgrade path that cost you an afternoon
No notes yet
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- Version or environment caveats, and links to real fixes
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