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TensorflowApplication · Google

CVE-2020-15202

CRITICAL · 9.0 CVSS v3.1 Published 2020-09-25
Fix available
A fix is available. Upgrade to 1.15.4 / 2.0.3 or later.
See remediation →
99/100
Remediation priority · Urgent
Public exploit Remotely reachable No privileges Zero-click Patch available

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
In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `Shard` API in TensorFlow expects the last argument to be a function taking two `int64` (i.e., `long long`) arguments. However, there are several places in TensorFlow where a lambda taking `int` or `int32` arguments is being used. In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption. The issue is patched in commits 27b417360cbd671ef55915e4bb6bb06af8b8a832 and ca8c013b5e97b1373b3bb1c97ea655e69f31a575, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

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

A detailed technical summary for this CVE is being prepared.

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:< 1.15.4>= 2.0.0, < 2.0.3>= 2.1.0, < 2.1.2>= 2.2.0, < 2.2.1>= 2.3.0, < 2.3.1
LeapOperating system
Affected:= 15.2

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
Network
Complexity
High
Privileges
None
User interaction
None
Scope
Changed
Confidentiality
High
Integrity
High
Availability
High

CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:H/I:H/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 · scoped
Upgrade available Upgrade to 1.15.4 / 2.0.3 / 2.1.2 or later
Fixed in 1.15.42.0.32.1.2
Vendor patch github.com →
Recommended fix High confidence

Upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1 (or latest stable) to resolve the integer truncation vulnerability in the Shard API

  1. Check current TensorFlow version by running: python -c "import tensorflow; print(tensorflow.__version__)"
  2. Upgrade TensorFlow to a fixed version using pip: pip install tensorflow==2.3.1 (or a version appropriate for your codebase)
  3. Verify the upgrade was successful: python -c "import tensorflow; print(tensorflow.__version__)"
  4. Test that existing TensorFlow workflows and models function correctly after the upgrade
Caveat TensorFlow minor version upgrades may have API changes; review release notes for any breaking changes between your current version and the target version

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

We can perform the upgrade in your staging environment and verify nothing breaks — typical engagement from $2,300. Get the upgrade done

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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-2020-15202 in production — separate from our analysis above.

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What this is

A place for practitioners to share what actually worked: a mitigation you’ve tested, a configuration change, a version- or environment-specific caveat, or a link to a verified patch. The most useful notes rise to the top as peers upvote them, so the signal stays high.

What belongs here
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  • Version or environment caveats, and links to real fixes
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