CVE-2021-29570 — HIGH (CVSS 7.1) AI Security Vulnerability

CISO Take

A low-complexity local vulnerability in TensorFlow's MaxPool gradient kernel allows attackers with minimal privileges to trigger heap out-of-bounds reads, risking memory disclosure or inference service crash. Patch immediately to TF 2.5.0 or apply the available backports to 2.1.4/2.2.3/2.3.3/2.4.2. Primary risk surface is shared ML inference infrastructure processing untrusted inputs.

Risk Assessment

CVSS 7.1 High with local attack vector and low privileges required. While local access limits opportunistic exploitation, shared ML infrastructure—multi-tenant Jupyter environments, model serving APIs, MLOps pipelines—effectively expands the attack surface beyond the nominal local vector. No confirmed active exploitation in the wild and not in CISA KEV. Risk is elevated for organizations running shared TensorFlow inference infrastructure without strict input validation or process isolation.

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 1% 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 2.5.0 or cherry-pick backport commit dcd7867de0fea4b72a2b34bd41eb74548dc23886 for affected branches (2.1.4, 2.2.3, 2.3.3, 2.4.2).
Inventory: Audit TF version across all inference servers, MLOps pipelines, Jupyter hubs, and CI/CD training runners.
Workaround: Restrict tf.raw_ops access to trusted callers; sandbox TensorFlow execution when processing untrusted inputs.
Detection: Alert on unexpected TF serving process crashes or anomalous OOM signals from inference services as potential exploitation indicators.

Classification

DoS Data Extraction 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 operation

NIST AI RMF

MANAGE-2.2 - Mechanisms to inventory and manage AI risks

OWASP LLM Top 10

LLM03:2025 - Supply Chain Vulnerabilities

Frequently Asked Questions

What is CVE-2021-29570?

A low-complexity local vulnerability in TensorFlow's MaxPool gradient kernel allows attackers with minimal privileges to trigger heap out-of-bounds reads, risking memory disclosure or inference service crash. Patch immediately to TF 2.5.0 or apply the available backports to 2.1.4/2.2.3/2.3.3/2.4.2. Primary risk surface is shared ML inference infrastructure processing untrusted inputs.

Is CVE-2021-29570 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2021-29570, increasing the risk of exploitation.

How to fix CVE-2021-29570?

1. Patch: Upgrade to TensorFlow 2.5.0 or cherry-pick backport commit dcd7867de0fea4b72a2b34bd41eb74548dc23886 for affected branches (2.1.4, 2.2.3, 2.3.3, 2.4.2). 2. Inventory: Audit TF version across all inference servers, MLOps pipelines, Jupyter hubs, and CI/CD training runners. 3. Workaround: Restrict tf.raw_ops access to trusted callers; sandbox TensorFlow execution when processing untrusted inputs. 4. Detection: Alert on unexpected TF serving process crashes or anomalous OOM signals from inference services as potential exploitation indicators.

What systems are affected by CVE-2021-29570?

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

What is the CVSS score for CVE-2021-29570?

CVE-2021-29570 has a CVSS v3.1 base score of 7.1 (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 `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/ef0c008ee84bad91ec6725ddc42091e19a30cf0e/tensorflow/core/kernels/maxpooling_op.cc#L1016-L1017) uses the same value to index in two different arrays but there is no guarantee that the sizes are identical. 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 multi-tenant ML platform or self-hosted model serving endpoint crafts a malicious inference request with mismatched tensor dimensions targeting the MaxPoolGradWithArgmax kernel. The kernel reads beyond its heap-allocated buffer—either crashing the TF serving process (DoS of the inference API) or leaking adjacent heap memory that may contain model weights, prior inference results, or session metadata. In CI/CD pipelines that auto-execute submitted models, an untrusted contributor could embed this exploit to disrupt training runs or probe memory contents of co-located workloads.