CVE-2021-41210 — HIGH (CVSS 7.1) AI Security Vulnerability

CISO Take

A heap out-of-bounds read in TensorFlow's SparseCountSparseOutput shape inference allows local low-privilege attackers to read sensitive memory or crash the process. Shared ML training clusters, multi-tenant Jupyter environments, and containerized inference platforms face the highest real-world exposure. Patch all TensorFlow deployments to 2.7.0, 2.6.1, 2.5.2, or 2.4.4 immediately and audit base images in ML CI/CD pipelines.

Risk Assessment

CVSS 7.1 High, but operational risk is moderate for most organizations due to the local attack vector. The vulnerability becomes significantly more dangerous in shared compute environments — GPU clusters, Jupyter hubs, or SageMaker Studio — where multiple users share the same TensorFlow runtime. Attack complexity is low once local access is obtained. No CISA KEV listing and no known active exploitation at time of disclosure, but the 2021 vintage means many unpatched deployments still exist in production.

Affected Systems

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

Do you use tensorflow? You're affected.

Severity & Risk

CVSS 3.1

7.1 / 10

EPSS

0.0%

chance of exploitation in 30 days

Higher than 5% 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 None

A High

Recommended Action

6 steps

Patch: Upgrade TensorFlow to 2.7.0, 2.6.1, 2.5.2, or 2.4.4 in all environments.
Inventory: Enumerate TF versions across training clusters, CI/CD pipelines, notebook servers, and inference containers. Use 'pip show tensorflow' or container scanning tools.
Container hygiene: Update Dockerfiles and requirements.txt/pyproject.toml constraints; rebuild and redeploy affected images.
Isolation: Until patched, enforce least-privilege access on shared ML compute — restrict who can submit arbitrary TF operations.
Detection: Alert on unexpected SIGSEGV/heap corruption crashes in TF training or serving processes.
Verify: Confirm patch application with CVE scanners (Trivy, Grype) against your ML container registry.

Classification

Data Extraction Code Execution DoS Framework Training Data AML.T0010.001 - AI Software AML.T0037 - Data from Local System AML.T0049 - Exploit Public-Facing Application

Compliance Impact

This CVE is relevant to:

EU AI Act

Art. 9 - Risk Management System

ISO 42001

A.6.2.6 - AI system security

NIST AI RMF

MANAGE 2.2 - Mechanisms are in place and applied to sustain the value of deployed AI systems

OWASP LLM Top 10

LLM03:2025 - Supply Chain

Frequently Asked Questions

What is CVE-2021-41210?

A heap out-of-bounds read in TensorFlow's SparseCountSparseOutput shape inference allows local low-privilege attackers to read sensitive memory or crash the process. Shared ML training clusters, multi-tenant Jupyter environments, and containerized inference platforms face the highest real-world exposure. Patch all TensorFlow deployments to 2.7.0, 2.6.1, 2.5.2, or 2.4.4 immediately and audit base images in ML CI/CD pipelines.

Is CVE-2021-41210 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2021-41210, increasing the risk of exploitation.

How to fix CVE-2021-41210?

1. Patch: Upgrade TensorFlow to 2.7.0, 2.6.1, 2.5.2, or 2.4.4 in all environments. 2. Inventory: Enumerate TF versions across training clusters, CI/CD pipelines, notebook servers, and inference containers. Use 'pip show tensorflow' or container scanning tools. 3. Container hygiene: Update Dockerfiles and requirements.txt/pyproject.toml constraints; rebuild and redeploy affected images. 4. Isolation: Until patched, enforce least-privilege access on shared ML compute — restrict who can submit arbitrary TF operations. 5. Detection: Alert on unexpected SIGSEGV/heap corruption crashes in TF training or serving processes. 6. Verify: Confirm patch application with CVE scanners (Trivy, Grype) against your ML container registry.

What systems are affected by CVE-2021-41210?

This vulnerability affects the following AI/ML architecture patterns: training pipelines, model serving, notebook environments, multi-tenant ML platforms, containerized ML workloads.

What is the CVSS score for CVE-2021-41210?

CVE-2021-41210 has a CVSS v3.1 base score of 7.1 (HIGH). The EPSS exploitation probability is 0.02%.

Technical Details

NVD Description

TensorFlow is an open source platform for machine learning. In affected versions the shape inference functions for `SparseCountSparseOutput` can trigger a read outside of bounds of heap allocated array. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

Exploitation Scenario

An adversary with low-privilege access to a shared ML training cluster — a compromised data scientist account or malicious insider — crafts a Python script that calls tf.sets.count() with a specially malformed sparse tensor. During shape inference for SparseCountSparseOutput, TensorFlow reads beyond the bounds of a heap-allocated array. In a training environment, adjacent heap memory may contain sensitive batch data, gradient tensors, or authentication tokens (e.g., cloud provider credentials loaded into the training process). Repeated triggering can also crash the training job, disrupting production model pipelines and causing financial loss in long-running distributed training runs.