CVE-2021-41212 — HIGH (CVSS 7.1) AI Security Vulnerability

CISO Take

Any TensorFlow deployment (training, serving, notebooks) running versions prior to 2.4.4/2.5.2/2.6.1/2.7.0 that processes ragged tensors is exposed to a local heap out-of-bounds read triggerable by crafted inputs to tf.ragged.cross. Patch immediately — if you run multi-tenant Jupyter/Colab environments or expose TF serving endpoints that accept user-controlled tensor shapes, the risk escalates significantly. Prioritize patching ML infrastructure where untrusted inputs can reach ragged tensor operations.

Risk Assessment

Effective risk is moderate-to-high for organizations with shared or externally-accessible ML infrastructure. The CVSS local attack vector (AV:L) limits scope for remote exploitation, but in practice TensorFlow Serving, Jupyter hubs, and ML pipeline APIs expose tensor shape processing to untrusted inputs — effectively elevating this to a network-reachable condition. AC:L (low complexity) means exploitation requires minimal skill once a vulnerable endpoint is identified. No CISA KEV listing and no public PoC weaponization as of disclosure, but the GitHub advisory tags it as 'Exploit' available.

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

7.1 / 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

4 steps

Patch: Upgrade to TensorFlow >=2.7.0, or apply cherrypick patches for 2.4.4, 2.5.2, 2.6.1. Pin versions in requirements.txt/conda envs and enforce in CI.
Immediate workaround: If patching is delayed, disable or sandbox endpoints that accept user-controlled ragged tensor shapes. Validate input tensor rank and shape bounds before passing to tf.ragged.cross.
Detection: Monitor for unexpected TF process crashes (SIGSEGV/SIGABRT) in serving infrastructure — repeated crashes against ragged ops are an indicator. Enable AddressSanitizer in dev/staging builds to catch OOB access during testing.
Inventory: Audit ML pipelines for use of tf.ragged.cross and related ragged ops; prioritize multi-tenant or externally-facing deployments.

Classification

DoS Data Extraction Code Execution Framework Training Data Inference AML.T0010.001 - AI Software AML.T0029 - Denial of AI Service AML.T0049 - Exploit Public-Facing Application

Compliance Impact

This CVE is relevant to:

EU AI Act

Article 9 - Risk management system for high-risk AI

ISO 42001

8.2 - AI risk assessment 8.4 - AI system operation and monitoring

NIST AI RMF

GOVERN-1.1 - Policies and processes for AI risk management MANAGE-2.2 - Mechanisms to sustain AI system reliability and safety

Frequently Asked Questions

What is CVE-2021-41212?

Any TensorFlow deployment (training, serving, notebooks) running versions prior to 2.4.4/2.5.2/2.6.1/2.7.0 that processes ragged tensors is exposed to a local heap out-of-bounds read triggerable by crafted inputs to tf.ragged.cross. Patch immediately — if you run multi-tenant Jupyter/Colab environments or expose TF serving endpoints that accept user-controlled tensor shapes, the risk escalates significantly. Prioritize patching ML infrastructure where untrusted inputs can reach ragged tensor operations.

Is CVE-2021-41212 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2021-41212, increasing the risk of exploitation.

How to fix CVE-2021-41212?

1. Patch: Upgrade to TensorFlow >=2.7.0, or apply cherrypick patches for 2.4.4, 2.5.2, 2.6.1. Pin versions in requirements.txt/conda envs and enforce in CI. 2. Immediate workaround: If patching is delayed, disable or sandbox endpoints that accept user-controlled ragged tensor shapes. Validate input tensor rank and shape bounds before passing to tf.ragged.cross. 3. Detection: Monitor for unexpected TF process crashes (SIGSEGV/SIGABRT) in serving infrastructure — repeated crashes against ragged ops are an indicator. Enable AddressSanitizer in dev/staging builds to catch OOB access during testing. 4. Inventory: Audit ML pipelines for use of tf.ragged.cross and related ragged ops; prioritize multi-tenant or externally-facing deployments.

What systems are affected by CVE-2021-41212?

This vulnerability affects the following AI/ML architecture patterns: training pipelines, model serving, shared ML notebooks, feature engineering pipelines.

What is the CVSS score for CVE-2021-41212?

CVE-2021-41212 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 code for `tf.ragged.cross` 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 access to a TensorFlow Serving endpoint or shared Jupyter environment submits inference requests or notebook code containing tf.ragged.cross calls with maliciously shaped input tensors. During shape inference — before any computation runs — the vulnerable code reads beyond the bounds of a heap-allocated array. In a multi-tenant ML platform, this crashes the TF worker process (denying service to other users) and may leak heap contents including portions of loaded model weights or co-located inference request buffers. In a training pipeline context, an insider or compromised CI/CD job injects the malformed op into a training graph, crashing distributed workers and potentially exfiltrating memory from the training coordinator process.