CVE-2021-29593 — HIGH (CVSS 7.8) AI Security Vulnerability

CISO Take

Any system that loads externally-supplied or user-provided TFLite models is at risk of crash or potential code execution (CVSS C:H/I:H/A:H). Patch TensorFlow to 2.5.0+ or the respective cherry-pick release for your branch. Audit model ingestion pipelines to ensure only trusted, validated models are loaded.

Risk Assessment

HIGH for ML inference services that accept external TFLite models. Local attack vector limits internet-exposed risk, but model-as-input threat is common in MLOps pipelines where models are downloaded from registries, shared over internal tools, or supplied by third parties. Trivial to exploit: attacker only needs to craft a model with a zero-dimension block input. CVSS 7.8 with full CIA impact suggests potential memory corruption beyond a clean crash.

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

7.8 / 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, 2.4.2, 2.3.3, 2.2.3, or 2.1.4 depending on your branch.
Model validation: enforce a model allow-list or hash verification before loading any TFLite file.
Sandboxing: run TFLite inference in a sandboxed process (seccomp, gVisor, container with no-new-privileges) to contain blast radius.
Detection: monitor for SIGFPE / divide-by-zero signals in inference processes; unexpected crashes in model serving workers should trigger incident response.
Supply chain: audit third-party model sources; prefer signed model artifacts.

Classification

Code Execution DoS Supply Chain Framework Inference AML.T0010.001 - AI Software AML.T0011 - User Execution AML.T0011.000 - Unsafe AI Artifacts AML.T0049 - Exploit Public-Facing Application

Compliance Impact

This CVE is relevant to:

EU AI Act

Article 15 - Accuracy, robustness and cybersecurity

ISO 42001

A.6.2 - AI system lifecycle security A.9.3 - AI system security testing

NIST AI RMF

GOVERN 6.1 - Policies for third-party AI risk MANAGE 2.4 - Residual risks and response

OWASP LLM Top 10

LLM05:2025 - Improper Output Handling / Insecure Plugin Design

Frequently Asked Questions

What is CVE-2021-29593?

Any system that loads externally-supplied or user-provided TFLite models is at risk of crash or potential code execution (CVSS C:H/I:H/A:H). Patch TensorFlow to 2.5.0+ or the respective cherry-pick release for your branch. Audit model ingestion pipelines to ensure only trusted, validated models are loaded.

Is CVE-2021-29593 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2021-29593, increasing the risk of exploitation.

How to fix CVE-2021-29593?

1. Patch: upgrade to TensorFlow 2.5.0, 2.4.2, 2.3.3, 2.2.3, or 2.1.4 depending on your branch. 2. Model validation: enforce a model allow-list or hash verification before loading any TFLite file. 3. Sandboxing: run TFLite inference in a sandboxed process (seccomp, gVisor, container with no-new-privileges) to contain blast radius. 4. Detection: monitor for SIGFPE / divide-by-zero signals in inference processes; unexpected crashes in model serving workers should trigger incident response. 5. Supply chain: audit third-party model sources; prefer signed model artifacts.

What systems are affected by CVE-2021-29593?

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

What is the CVSS score for CVE-2021-29593?

CVE-2021-29593 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 implementation of the `BatchToSpaceNd` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/b5ed552fe55895aee8bd8b191f744a069957d18d/tensorflow/lite/kernels/batch_to_space_nd.cc#L81-L82). An attacker can craft a model such that one dimension of the `block` input is 0. Hence, the corresponding value in `block_shape` is 0. 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 ML inference API or MLOps pipeline crafts a minimal TFLite flatbuffer where the BatchToSpaceNd op has a block tensor with at least one zero dimension. They submit this model through a model upload endpoint, a shared model registry, or via a compromised model dependency. When the inference runtime processes the model, block_shape[i] = 0 triggers a division by zero at kernels/batch_to_space_nd.cc:81-82. Depending on platform and ASAN state, this crashes the inference worker (DoS) or, on unprotected targets, may allow heap/stack exploitation consistent with the CVSS C:H/I:H/A:H rating. In a model-serving SaaS context, a single crafted model could take down a shared inference worker affecting all tenants.