CVE-2021-29527 — MEDIUM (CVSS 5.5) AI Security Vulnerability

CISO Take

A local attacker with low privileges can crash any TensorFlow process by passing a zero-value divisor to QuantizedConv2D, causing a denial of service. Patch to TF 2.5.0 or the applicable backport (2.4.2, 2.3.3, 2.2.3, 2.1.4) immediately across all training and inference infrastructure. Risk is elevated in shared ML platforms and MLOps pipelines where multiple users submit jobs to a common environment.

Risk Assessment

Medium risk overall, but elevated in multi-tenant ML environments. Local-only exploitation limits remote attack surface, and exploitation requires minimal skill—just crafting a raw op call with a zero-value parameter. No confidentiality or integrity impact; availability of ML workloads is the sole concern. The real threat is disruption of shared training infrastructure where a single low-privileged user can crash processes affecting all tenants.

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 1% 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

4 steps

Patch: Upgrade to TensorFlow 2.5.0, 2.4.2, 2.3.3, 2.2.3, or 2.1.4 — all contain the fix.
Input validation: Ensure caller-supplied range parameters to QuantizedConv2D (min_input, max_input, min_filter, max_filter) cannot produce a zero divisor before reaching the kernel.
Access control: Restrict access to tf.raw_ops in multi-tenant environments using job isolation, containerization, or sandboxing to prevent cross-tenant disruption.
Detection: Monitor for SIGFPE/SIGABRT crashes in TF worker processes and alert on unexpected model evaluation failures involving QuantizedConv2D ops.

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

8.4 - AI System Availability and Resilience

NIST AI RMF

MANAGE-2.2 - Risks or Incidents Are Responded to and Recovered From

OWASP LLM Top 10

LLM03 - Supply Chain Vulnerabilities

Frequently Asked Questions

What is CVE-2021-29527?

A local attacker with low privileges can crash any TensorFlow process by passing a zero-value divisor to QuantizedConv2D, causing a denial of service. Patch to TF 2.5.0 or the applicable backport (2.4.2, 2.3.3, 2.2.3, 2.1.4) immediately across all training and inference infrastructure. Risk is elevated in shared ML platforms and MLOps pipelines where multiple users submit jobs to a common environment.

Is CVE-2021-29527 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2021-29527, increasing the risk of exploitation.

How to fix CVE-2021-29527?

1. Patch: Upgrade to TensorFlow 2.5.0, 2.4.2, 2.3.3, 2.2.3, or 2.1.4 — all contain the fix. 2. Input validation: Ensure caller-supplied range parameters to QuantizedConv2D (min_input, max_input, min_filter, max_filter) cannot produce a zero divisor before reaching the kernel. 3. Access control: Restrict access to tf.raw_ops in multi-tenant environments using job isolation, containerization, or sandboxing to prevent cross-tenant disruption. 4. Detection: Monitor for SIGFPE/SIGABRT crashes in TF worker processes and alert on unexpected model evaluation failures involving QuantizedConv2D ops.

What systems are affected by CVE-2021-29527?

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

What is the CVSS score for CVE-2021-29527?

CVE-2021-29527 has a CVSS v3.1 base score of 5.5 (MEDIUM). The EPSS exploitation probability is 0.01%.

Technical Details

NVD Description

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a division by 0 in `tf.raw_ops.QuantizedConv2D`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/00e9a4d67d76703fa1aee33dac582acf317e0e81/tensorflow/core/kernels/quantized_conv_ops.cc#L257-L259) does a division by a quantity that is controlled by the caller. 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 adversary with access to a shared ML training cluster (e.g., a data scientist on a multi-tenant Kubeflow deployment, or a malicious model contributor in an automated MLOps pipeline) submits a TensorFlow job that calls tf.raw_ops.QuantizedConv2D with min_input equal to max_input, producing a zero divisor in the kernel's range normalization step. This immediately crashes the TF process — potentially taking down a shared parameter server, disrupting co-located training runs, or blocking an automated model validation gate in a CI/CD pipeline before a production deployment.