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LOW severity

GHSA-393f-2jr3-cp69

LOWFix: tensorflow/tensorflow@b432a38

GHSA-393f-2jr3-cp69 is a low-severity (CVSS 2.5) CWE-754 vulnerability in tensorflow. 1 public exploit reference exists, so weaponization risk is real. O3 Security confirms whether GHSA-393f-2jr3-cp69 is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.

CHECK-fail in DrawBoundingBoxes

Also known asBIT-tensorflow-2021-29533CVE-2021-29533PYSEC-2021-170PYSEC-2021-461PYSEC-2021-659
Published
May 21, 2021
Updated
Jul 8, 2026
Affected
12 pkgs
Patched
12 / 12
Exploits
1 known
Exploitation data as of Jul 8, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

Real-World Exposure

12 pkgs affected
🐍tensorflow🐍tensorflow🐍tensorflow🐍tensorflow🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-cpu+4 more

Real-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.

Description

Impact

An attacker can trigger a denial of service via a CHECK failure by passing an empty image to tf.raw_ops.DrawBoundingBoxes:

import tensorflow as tf

images = tf.fill([53, 0, 48, 1], 0.)
boxes = tf.fill([53, 31, 4], 0.)
boxes = tf.Variable(boxes)
boxes[0, 0, 0].assign(3.90621)
tf.raw_ops.DrawBoundingBoxes(images=images, boxes=boxes)

This is because the implementation uses CHECK_* assertions instead of OP_REQUIRES to validate user controlled inputs. Whereas OP_REQUIRES allows returning an error condition back to the user, the CHECK_* macros result in a crash if the condition is false, similar to assert.

const int64 max_box_row_clamp = std::min<int64>(max_box_row, height - 1);
... 
CHECK_GE(max_box_row_clamp, 0);

In this case, height is 0 from the images input. This results in max_box_row_clamp being negative and the assertion being falsified, followed by aborting program execution.

Patches

We have patched the issue in GitHub commit b432a38fe0e1b4b904a6c222cbce794c39703e87.

The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team.

Affected Packages

12 total 12 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPItensorflowall versions2.1.4
🐍PyPItensorflow2.2.0&&< 2.2.32.2.3
🐍PyPItensorflow2.3.0&&< 2.3.32.3.3
🐍PyPItensorflow2.4.0&&< 2.4.22.4.2
🐍PyPItensorflow-cpuall versions2.1.4
🐍PyPItensorflow-cpu2.2.0&&< 2.2.32.2.3
Exploits & PoCs
1

Research use only. For defensive security, authorized penetration testing, and academic research only. Never execute exploit code against systems without explicit written authorization.

Detection & mitigation playbook

Open-source dependency
  1. Detect

    Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for tensorflow. O3's reachability analysis confirms whether the vulnerable code path is actually invoked in your application, so you act on real exposure instead of every transitive match.

  2. Fix

    Update tensorflow to 2.1.4 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-393f-2jr3-cp69 is resolved across your whole dependency graph.

  3. Workarounds

    If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.

  4. How O3 protects you

    O3 pinpoints whether GHSA-393f-2jr3-cp69 is reachable in your code and exactly where to fix it, then blocks exploitation in production at runtime until the patched version is deployed.

Tailored to GHSA-393f-2jr3-cp69. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.

Frequently Asked Questions

### Impact An attacker can trigger a denial of service via a `CHECK` failure by passing an empty image to `tf.raw_ops.DrawBoundingBoxes`: ```python import tensorflow as tf images = tf.fill([53, 0, 48, 1], 0.) boxes = tf.fill([53, 31, 4], 0.) boxes = tf.Variable(boxes) boxes[0, 0, 0].assign(3.90621) tf.raw_ops.DrawBoundingBoxes(images=images, boxes=boxes) ``` This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/ea34a18dc3f5c8d80a40ccca1404f343b5d55f91/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L148-L165) uses `CHECK_*` assertions instead of `OP_REQUIR
O3 Security · Impact-Aware SCA

Is GHSA-393f-2jr3-cp69 in your dependencies?

O3 detects GHSA-393f-2jr3-cp69 across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.