CVE-2022-21730
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 Machine Learning Framework. The implementation of `FractionalAvgPoolGrad` does not consider cases where the input tensors are invalid allowing an attacker to read from outside of bounds of heap. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
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 reads past the end (or before the start) of a buffer, returning memory that was never meant to be exposed. Attackers use it to leak secrets like keys or to defeat memory-protection defences. Remediation is validating indices and lengths before every read.
General guidance for the out-of-bounds read 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<= 2.5.2>= 2.6.0, <= 2.6.2= 2.7.0CVSS 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
- Low
- User interaction
- None
- Scope
- Unchanged
- Confidentiality
- High
- Integrity
- None
- Availability
- High
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/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 · scopedTensorFlow 2.8.0 (or alternatively 2.7.1, 2.6.3, or 2.5.3)
- 1. Identify the current TensorFlow version in your environment using `pip show tensorflow` or checking your dependency files
- 2. Determine which upgrade path suits your compatibility needs: either upgrade to 2.8.0 (latest fix), or to a cherry-patched version (2.7.1, 2.6.3, or 2.5.3) if you need to stay on a specific minor release
- 3. Upgrade TensorFlow using pip: `pip install --upgrade tensorflow==2.8.0` (or the version of your choice)
- 4. Verify the installation was successful: `python -c "import tensorflow as tf; print(tf.__version__)"`
- 5. Run your existing test suite to confirm functionality is not broken
- 6. If using TensorFlow Serving or other TensorFlow-derived projects, ensure those are also updated to compatible versions
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-2022-21730 — 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-2022-21730 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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- Verified mitigations, workarounds, and config changes
- Version or environment caveats, and links to real fixes
- No weaponised exploit code, or anything meant to cause harm
- No spam, self-promotion, credentials, or personal data