CVE-2020-15208
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 · uneditedIn tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when determining the common dimension size of two tensors, TFLite uses a `DCHECK` which is no-op outside of debug compilation modes. Since the function always returns the dimension of the first tensor, malicious attackers can craft cases where this is larger than that of the second tensor. In turn, this would result in reads/writes outside of bounds since the interpreter will wrongly assume that there is enough data in both tensors. The issue is patched in commit 8ee24e7949a203d234489f9da2c5bf45a7d5157d, 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 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< 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= 15.2CVSS 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
- High
- Integrity
- High
- Availability
- High
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/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 · scoped1.15.42.0.32.1.2
TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1 (or later stable release)
- Identify the currently installed TensorFlow version using 'pip show tensorflow' or 'pip list | grep tensorflow'
- Upgrade TensorFlow to a patched version based on your current major version line
- For TensorFlow 1.x: upgrade to 1.15.4 or later using 'pip install --upgrade tensorflow==1.15.4'
- For TensorFlow 2.0.x: upgrade to 2.0.3 or later using 'pip install --upgrade tensorflow==2.0.3'
- For TensorFlow 2.1.x: upgrade to 2.1.2 or later using 'pip install --upgrade tensorflow==2.1.2'
- For TensorFlow 2.2.x: upgrade to 2.2.1 or later using 'pip install --upgrade tensorflow==2.2.1'
- For TensorFlow 2.3.x: upgrade to 2.3.1 or later using 'pip install --upgrade tensorflow==2.3.1'
- Verify the installation using 'python -c "import tensorflow as tf; print(tf.__version__)"'
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-2020-15208 — 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-2020-15208 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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