CVE-2021-29579 — HIGH (CVSS 7.8) AI Security Vulnerability

CISO Take

TensorFlow's MaxPoolGrad operation contains a heap buffer overflow (OOB write) exploitable by any local user with low privileges — a serious risk in shared ML training clusters and multi-tenant Jupyter environments. Patch immediately to TF 2.5.0 or the respective backport (2.4.2, 2.3.3, 2.2.3, 2.1.4). Treat unpatched TensorFlow training nodes as high-risk lateral movement surfaces, particularly in Kubernetes-based ML pipelines where service accounts carry elevated permissions.

Risk Assessment

CVSS 7.8 HIGH with local attack vector, low complexity, and low privileges required. While remote exploitation requires indirect access (malicious notebook submission, shared cluster), the combination of low complexity and full C/I/A impact makes this dangerous in multi-tenant ML environments. Not in CISA KEV; no evidence of active in-the-wild exploitation. Risk is materially elevated for organizations running shared GPU/TPU training infrastructure where multiple users submit jobs to the same nodes.

Affected Systems

Package	Ecosystem	Vulnerable Range	Patched
tensorflow	pip	—	No patch
195.0K OpenSSF 7.2 3.7K dependents Pushed 6d ago 4% patched ~1372d to patch Full package profile →

Do you use tensorflow? You're affected.

Severity & Risk

CVSS 3.1

7.8 / 10

EPSS

0.0%

chance of exploitation in 30 days

Higher than 2% of all CVEs

Source: EPSS v3 — FIRST.org

Exploitation Status

Exploit Available

Exploitation: MEDIUM

Sophistication

Moderate

Exploitation Confidence

medium

○ Public PoC indexed (trickest/cve)

Composite signal derived from CISA KEV, CISA SSVC, EPSS, trickest/cve, and Nuclei templates.

Attack Surface

AV Local

AC Low

PR Low

UI None

S Unchanged

C High

I High

A High

Recommended Action

6 steps

Upgrade TensorFlow to 2.5.0, 2.4.2, 2.3.3, 2.2.3, or 2.1.4 depending on your current branch — confirm with python -c 'import tensorflow as tf; print(tf.__version__)' on all hosts.
Inventory all TensorFlow deployments: include training clusters, CI/CD pipelines, notebook servers, and model serving containers.
Apply network segmentation to isolate ML training nodes from production systems.
Restrict local shell access to training nodes — audit which accounts have SSH/console access and apply least privilege.
Enable process and file integrity monitoring on TF training hosts.
For Kubernetes ML pipelines: audit service account permissions on nodes running TensorFlow training jobs.

Classification

Code Execution Framework Inference AML.T0010.001 - AI Software AML.T0049 - Exploit Public-Facing Application

Compliance Impact

This CVE is relevant to:

EU AI Act

Art. 17 - Quality management system — technical robustness

ISO 42001

A.6.2.6 - AI system lifecycle — software component integrity

NIST AI RMF

MANAGE 2.2 - Mechanisms to sustain the value of deployed AI

Frequently Asked Questions

What is CVE-2021-29579?

TensorFlow's MaxPoolGrad operation contains a heap buffer overflow (OOB write) exploitable by any local user with low privileges — a serious risk in shared ML training clusters and multi-tenant Jupyter environments. Patch immediately to TF 2.5.0 or the respective backport (2.4.2, 2.3.3, 2.2.3, 2.1.4). Treat unpatched TensorFlow training nodes as high-risk lateral movement surfaces, particularly in Kubernetes-based ML pipelines where service accounts carry elevated permissions.

Is CVE-2021-29579 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2021-29579, increasing the risk of exploitation.

How to fix CVE-2021-29579?

1. Upgrade TensorFlow to 2.5.0, 2.4.2, 2.3.3, 2.2.3, or 2.1.4 depending on your current branch — confirm with `python -c 'import tensorflow as tf; print(tf.__version__)'` on all hosts. 2. Inventory all TensorFlow deployments: include training clusters, CI/CD pipelines, notebook servers, and model serving containers. 3. Apply network segmentation to isolate ML training nodes from production systems. 4. Restrict local shell access to training nodes — audit which accounts have SSH/console access and apply least privilege. 5. Enable process and file integrity monitoring on TF training hosts. 6. For Kubernetes ML pipelines: audit service account permissions on nodes running TensorFlow training jobs.

What systems are affected by CVE-2021-29579?

This vulnerability affects the following AI/ML architecture patterns: training pipelines, model serving, shared notebook environments, ML training clusters.

What is the CVSS score for CVE-2021-29579?

CVE-2021-29579 has a CVSS v3.1 base score of 7.8 (HIGH). The EPSS exploitation probability is 0.01%.

Technical Details

NVD Description

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/ab1e644b48c82cb71493f4362b4dd38f4577a1cf/tensorflow/core/kernels/maxpooling_op.cc#L194-L203) fails to validate that indices used to access elements of input/output arrays are valid. Whereas accesses to `input_backprop_flat` are guarded by `FastBoundsCheck`, the indexing in `out_backprop_flat` can result in OOB access. 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.

Exploitation Scenario

An attacker with low-privilege shell access to a shared GPU training server — for example, a data scientist account on a multi-tenant ML cluster — crafts a malicious TensorFlow script invoking `tf.raw_ops.MaxPoolGrad` with specially crafted tensor shapes that trigger the OOB write in `out_backprop_flat`. The heap corruption enables arbitrary code execution in the TensorFlow process context. If the training job runs under a Kubernetes service account with broad permissions (common in MLOps pipelines), the attacker escalates to cluster-wide access — enabling trained model exfiltration, training data theft, or lateral movement to adjacent production systems sharing the cluster.