CVE-2021-29532 — HIGH (CVSS 7.1) AI Security Vulnerability

CISO Take

TensorFlow's RaggedCross kernel operation lacks bounds validation on user-supplied tensor indices, enabling heap out-of-bounds reads that can disclose adjacent process memory or crash ML workloads. Any environment where untrusted users submit TensorFlow operations — shared Jupyter clusters, TF Serving endpoints — is the primary exposure surface. Patch immediately to TF 2.5.0, 2.4.2, 2.3.3, 2.2.3, or 2.1.4.

Risk Assessment

Moderate-to-high risk for multi-tenant ML infrastructure. CVSS 7.1 with local attack vector and low-privilege requirement means shared training clusters and notebook environments are the primary blast radius. Confidentiality impact is high (heap memory disclosure) and availability impact is high (process crash). Not in CISA KEV with no reported active exploitation, reducing immediate urgency, but low attack complexity makes this trivially weaponizable by any authorized platform user.

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

5 steps

Patch: Upgrade to TensorFlow 2.5.0, or apply cherry-picked fixes in 2.4.2, 2.3.3, 2.2.3, or 2.1.4.
Workaround: Restrict access to tf.raw_ops.RaggedCross via TF op allowlisting in serving environments if patching is delayed.
Network controls: Do not expose raw TF op execution to untrusted clients; restrict TF Serving endpoints to authenticated internal consumers only.
Detection: Monitor for abnormal process crashes or heap corruption signals (SIGSEGV, abort traps) in TF workloads.
Container isolation: Run TF workloads in isolated containers per-user or per-tenant to limit memory disclosure blast radius.

Classification

Data Extraction DoS Framework Inference AML.T0010.001 - AI Software AML.T0040 - AI Model Inference API Access AML.T0049 - Exploit Public-Facing Application

Compliance Impact

This CVE is relevant to:

EU AI Act

Article 9 - Risk management system

ISO 42001

A.6.2.5 - AI system testing and validation

NIST AI RMF

MANAGE 2.2 - Mechanisms to sustain the value of deployed AI systems

Frequently Asked Questions

What is CVE-2021-29532?

TensorFlow's RaggedCross kernel operation lacks bounds validation on user-supplied tensor indices, enabling heap out-of-bounds reads that can disclose adjacent process memory or crash ML workloads. Any environment where untrusted users submit TensorFlow operations — shared Jupyter clusters, TF Serving endpoints — is the primary exposure surface. Patch immediately to TF 2.5.0, 2.4.2, 2.3.3, 2.2.3, or 2.1.4.

Is CVE-2021-29532 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2021-29532, increasing the risk of exploitation.

How to fix CVE-2021-29532?

1. Patch: Upgrade to TensorFlow 2.5.0, or apply cherry-picked fixes in 2.4.2, 2.3.3, 2.2.3, or 2.1.4. 2. Workaround: Restrict access to tf.raw_ops.RaggedCross via TF op allowlisting in serving environments if patching is delayed. 3. Network controls: Do not expose raw TF op execution to untrusted clients; restrict TF Serving endpoints to authenticated internal consumers only. 4. Detection: Monitor for abnormal process crashes or heap corruption signals (SIGSEGV, abort traps) in TF workloads. 5. Container isolation: Run TF workloads in isolated containers per-user or per-tenant to limit memory disclosure blast radius.

What systems are affected by CVE-2021-29532?

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

What is the CVSS score for CVE-2021-29532?

CVE-2021-29532 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. An attacker can force accesses outside the bounds of heap allocated arrays by passing in invalid tensor values to `tf.raw_ops.RaggedCross`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/efea03b38fb8d3b81762237dc85e579cc5fc6e87/tensorflow/core/kernels/ragged_cross_op.cc#L456-L487) lacks validation for the user supplied arguments. Each of the above branches call a helper function after accessing array elements via a `*_list[next_*]` pattern, followed by incrementing the `next_*` index. However, as there is no validation that the `next_*` values are in the valid range for the corresponding `*_list` arrays, this results in heap OOB reads. 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 low-privilege access to a shared ML training platform (e.g., corporate JupyterHub or ML experimentation environment) submits a crafted notebook calling tf.raw_ops.RaggedCross with deliberately invalid tensor index values. Missing bounds validation on next_* counters causes heap OOB reads into adjacent memory — potentially leaking model weights, training batch data, or authentication tokens from co-tenant sessions sharing the same Python process. In a TF Serving deployment, a client can send crafted gRPC inference requests with malformed RaggedTensor payloads to repeatedly crash the serving process, causing sustained availability impact on ML-powered downstream applications.