CVE-2021-41222 — MEDIUM (CVSS 5.5) AI Security Vulnerability

Q: Is CVE-2021-41222 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2021-41222, increasing the risk of exploitation.

Q: How to fix CVE-2021-41222?

1. Patch: Upgrade to TensorFlow 2.7.0, or apply cherrypick patches to 2.6.1, 2.5.2, or 2.4.4. 2. Input validation: Add pre-execution checks enforcing that all values in size_splits are strictly non-negative before invoking SplitV. 3. Process isolation: Run TF inference in isolated containers/processes with automatic restart policies (e.g., Kubernetes restartPolicy: Always) to contain crash blast radius. 4. Detection: Monitor for unexpected SIGSEGV terminations in TF serving processes — repeated crashes on inference endpoints may indicate active probing. 5. Audit: Identify all code paths where size_splits values originate from user-controlled input and add sanitization at those ingestion points.

Q: What systems are affected by CVE-2021-41222?

This vulnerability affects the following AI/ML architecture patterns: model serving, training pipelines, inference.

Q: What is the CVSS score for CVE-2021-41222?

CVE-2021-41222 has a CVSS v3.1 base score of 5.5 (MEDIUM). The EPSS exploitation probability is 0.02%.

CISO Take

A malformed negative value in TensorFlow's SplitV operation triggers a segmentation fault, crashing any process running affected versions (< 2.7.0). Patch as part of your next routine TF upgrade cycle — this is not an emergency unless TensorFlow inference is directly exposed to untrusted user inputs. Risk is limited to availability; there is no evidence of data exfiltration or code execution potential.

Risk Assessment

CVSS 5.5 (Medium) reflects the constrained local attack vector — exploitation requires the ability to influence TF operation inputs, which limits exposure significantly. Highest risk surfaces are multi-tenant ML platforms (shared Jupyter environments, CI/CD pipelines accepting user-submitted models) and TF-Serving deployments that process user-controlled tensor shapes. Not in CISA KEV, no active exploitation reported. Treatment: include in routine patching; do not escalate to emergency response unless exposure profile is unusually high.

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

5.5 / 10

EPSS

0.0%

chance of exploitation in 30 days

Higher than 4% of all CVEs

Source: EPSS v3 — FIRST.org

Exploitation Status

Exploit Available

Exploitation: MEDIUM

Sophistication

Trivial

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 None

I None

A High

Recommended Action

5 steps

Patch: Upgrade to TensorFlow 2.7.0, or apply cherrypick patches to 2.6.1, 2.5.2, or 2.4.4.
Input validation: Add pre-execution checks enforcing that all values in size_splits are strictly non-negative before invoking SplitV.
Process isolation: Run TF inference in isolated containers/processes with automatic restart policies (e.g., Kubernetes restartPolicy: Always) to contain crash blast radius.
Detection: Monitor for unexpected SIGSEGV terminations in TF serving processes — repeated crashes on inference endpoints may indicate active probing.
Audit: Identify all code paths where size_splits values originate from user-controlled input and add sanitization at those ingestion points.

Classification

DoS Framework 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

Art. 15 - Accuracy, Robustness and Cybersecurity

ISO 42001

A.6.1.6 - AI system operation and monitoring

NIST AI RMF

MEASURE 2.5 - AI System Robustness and Reliability

OWASP LLM Top 10

LLM04 - Model Denial of Service

Frequently Asked Questions

What is CVE-2021-41222?

A malformed negative value in TensorFlow's SplitV operation triggers a segmentation fault, crashing any process running affected versions (< 2.7.0). Patch as part of your next routine TF upgrade cycle — this is not an emergency unless TensorFlow inference is directly exposed to untrusted user inputs. Risk is limited to availability; there is no evidence of data exfiltration or code execution potential.

Is CVE-2021-41222 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2021-41222, increasing the risk of exploitation.

How to fix CVE-2021-41222?

1. Patch: Upgrade to TensorFlow 2.7.0, or apply cherrypick patches to 2.6.1, 2.5.2, or 2.4.4. 2. Input validation: Add pre-execution checks enforcing that all values in size_splits are strictly non-negative before invoking SplitV. 3. Process isolation: Run TF inference in isolated containers/processes with automatic restart policies (e.g., Kubernetes restartPolicy: Always) to contain crash blast radius. 4. Detection: Monitor for unexpected SIGSEGV terminations in TF serving processes — repeated crashes on inference endpoints may indicate active probing. 5. Audit: Identify all code paths where size_splits values originate from user-controlled input and add sanitization at those ingestion points.

What systems are affected by CVE-2021-41222?

This vulnerability affects the following AI/ML architecture patterns: model serving, training pipelines, inference.

What is the CVSS score for CVE-2021-41222?

CVE-2021-41222 has a CVSS v3.1 base score of 5.5 (MEDIUM). The EPSS exploitation probability is 0.02%.

Technical Details

NVD Description

TensorFlow is an open source platform for machine learning. In affected versions the implementation of `SplitV` can trigger a segfault is an attacker supplies negative arguments. This occurs whenever `size_splits` contains more than one value and at least one value is negative. 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 attacker with access to a TF-Serving inference endpoint crafts a request invoking SplitV with size_splits containing two or more values where at least one is negative (e.g., [-1, 5]). TensorFlow fails to validate the negative entry, triggering a segfault that crashes the server process. In a shared ML notebook environment (Jupyter Hub, SageMaker Studio), a malicious collaborator submits a notebook cell with a crafted SplitV call, crashing the kernel and disrupting co-workers. In a CI/CD pipeline that evaluates community-contributed models, a poisoned model embedding a SplitV call with negative splits could crash the evaluation runner, delaying deployments or silently skipping security gates.