CVE-2021-29587 — HIGH (CVSS 7.8) AI Security Vulnerability

CISO Take

Any environment loading untrusted TFLite models is exposed to a crash or potential code execution via a crafted SpaceToDepth operator with block_size=0. The primary risk vector is AI/ML pipelines or mobile deployments that ingest externally-sourced models. Patch immediately to TF 2.5.0, 2.4.2, 2.3.3, 2.2.3, or 2.1.4 and audit model provenance controls.

Risk Assessment

CVSS 7.8 High but local attack vector constrains real-world risk somewhat. Exploitation is trivial — an attacker only needs to craft a TFLite model with a zero block_size parameter and get a target system to load it. Risk elevates significantly in pipelines that consume third-party or user-supplied models without validation, common in MLOps and edge deployment scenarios. No evidence of active exploitation in the wild, but the technique is easily weaponizable.

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

7.8 / 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 High

I High

A High

Recommended Action

5 steps

Patch: Upgrade to TensorFlow 2.5.0 or cherry-picked fixes in 2.4.2, 2.3.3, 2.2.3, 2.1.4.
Model validation: Implement pre-load validation of TFLite models — reject models with block_size=0 in SpaceToDepth ops before inference.
Isolation: Run TFLite inference in sandboxed processes so a crash doesn't propagate to the host system.
Provenance controls: Enforce cryptographic signing and allowlisting of approved model sources.
Detection: Alert on process crashes in inference services; log model hashes at load time for forensic tracing.

Classification

Supply Chain DoS Code Execution Framework Model Inference AML.T0010.001 - AI Software AML.T0011.000 - Unsafe AI Artifacts AML.T0018 - Manipulate AI Model 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

6.1.2 - AI risk assessment 8.4 - AI system operation

NIST AI RMF

GOVERN-6.2 - Policies and procedures are in place for AI risk management across the supply chain MANAGE-2.2 - Mechanisms to sustain the value of deployed AI systems are evaluated and in place

Frequently Asked Questions

What is CVE-2021-29587?

Any environment loading untrusted TFLite models is exposed to a crash or potential code execution via a crafted SpaceToDepth operator with block_size=0. The primary risk vector is AI/ML pipelines or mobile deployments that ingest externally-sourced models. Patch immediately to TF 2.5.0, 2.4.2, 2.3.3, 2.2.3, or 2.1.4 and audit model provenance controls.

Is CVE-2021-29587 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2021-29587, increasing the risk of exploitation.

How to fix CVE-2021-29587?

1. Patch: Upgrade to TensorFlow 2.5.0 or cherry-picked fixes in 2.4.2, 2.3.3, 2.2.3, 2.1.4. 2. Model validation: Implement pre-load validation of TFLite models — reject models with block_size=0 in SpaceToDepth ops before inference. 3. Isolation: Run TFLite inference in sandboxed processes so a crash doesn't propagate to the host system. 4. Provenance controls: Enforce cryptographic signing and allowlisting of approved model sources. 5. Detection: Alert on process crashes in inference services; log model hashes at load time for forensic tracing.

What systems are affected by CVE-2021-29587?

This vulnerability affects the following AI/ML architecture patterns: edge/mobile AI deployments, model serving, training pipelines, MLOps model validation pipelines, embedded AI systems.

What is the CVSS score for CVE-2021-29587?

CVE-2021-29587 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 `Prepare` step of the `SpaceToDepth` TFLite operator does not check for 0 before division(https://github.com/tensorflow/tensorflow/blob/5f7975d09eac0f10ed8a17dbb6f5964977725adc/tensorflow/lite/kernels/space_to_depth.cc#L63-L67). An attacker can craft a model such that `params->block_size` would be zero. 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 targeting an MLOps pipeline or mobile AI backend crafts a TFLite model file with a SpaceToDepth layer where block_size is set to zero. The model is published to a public model hub or injected into a supply chain (e.g., a compromised model registry or poisoned dependency). When the victim system loads the model for inference or validation, the TFLite runtime hits the unguarded division in the Prepare step, triggering a crash. In edge deployments on IoT or mobile devices, this enables persistent denial-of-service or, if memory corruption is exploitable on the target platform, potential code execution with the privileges of the inference process.