CVE-2023-33976 — HIGH (CVSS 7.5) AI Security Vulnerability

CISO Take

An unauthenticated remote attacker can crash any TensorFlow service that processes user-supplied tensors through array_ops.upper_bound by sending a non-rank-2 tensor, causing an immediate segfault and service disruption. If your organization exposes TensorFlow inference endpoints to untrusted input, this is exploitable with trivial effort. Patch to TensorFlow 2.12 (cherrypick) or 2.13+ immediately; add tensor shape validation at API boundaries as a defense-in-depth measure.

Risk Assessment

Risk is HIGH for externally-exposed TensorFlow serving infrastructure. The CVSS vector (AV:N/AC:L/PR:N/UI:N) means no authentication, no special knowledge, and network-accessible — a script-kiddie can trigger this. Impact is availability only (no data exfiltration), but crashing a production inference service can cascade into business disruption. Not in CISA KEV and no confirmed active exploitation, which reduces urgency slightly. Organizations running TensorFlow behind authentication layers or with strict input validation have materially lower exposure.

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

7.5 / 10

EPSS

0.0%

chance of exploitation in 30 days

Higher than 11% of all CVEs

Source: EPSS v3 — FIRST.org

Exploitation Status

No known exploitation

Sophistication

Trivial

Attack Surface

AV Network

AC Low

PR None

UI None

S Unchanged

C None

I None

A High

Recommended Action

5 steps

PATCH

Upgrade to TensorFlow 2.13+ or apply the cherry-pick to TensorFlow 2.12 (commits 6fa05df and 915884f).
VALIDATE

Add explicit tensor rank checks before any call to array_ops.upper_bound — assert input.shape.rank == 2.
ISOLATE

Run TensorFlow serving processes with resource limits (ulimits, cgroups) to contain crash blast radius and enable auto-restart.
MONITOR

Alert on abnormal segfault/OOM kill rates in model serving containers — repeated crashes pattern may indicate active probing.
HARDEN

Deploy input schema validation (shape, dtype, rank) at API gateway layer before requests reach TensorFlow runtime.

CISA SSVC Assessment

Decision Track

Exploitation none

Automatable No

Technical Impact partial

Source: CISA Vulnrichment (SSVC v2.0). Decision based on the CISA Coordinator decision tree.

Classification

DoS Framework Inference AML.T0029 - Denial of AI Service AML.T0034 - Cost Harvesting AML.T0049 - Exploit Public-Facing Application

Compliance Impact

This CVE is relevant to:

EU AI Act

Article 15 - Accuracy, robustness and cybersecurity

ISO 42001

8.4 - AI system operation

NIST AI RMF

MANAGE 2.2 - Mechanisms to sustain AI system performance and reliability

OWASP LLM Top 10

LLM04 - Model Denial of Service

Frequently Asked Questions

What is CVE-2023-33976?

An unauthenticated remote attacker can crash any TensorFlow service that processes user-supplied tensors through array_ops.upper_bound by sending a non-rank-2 tensor, causing an immediate segfault and service disruption. If your organization exposes TensorFlow inference endpoints to untrusted input, this is exploitable with trivial effort. Patch to TensorFlow 2.12 (cherrypick) or 2.13+ immediately; add tensor shape validation at API boundaries as a defense-in-depth measure.

Is CVE-2023-33976 actively exploited?

No confirmed active exploitation of CVE-2023-33976 has been reported, but organizations should still patch proactively.

How to fix CVE-2023-33976?

1. PATCH: Upgrade to TensorFlow 2.13+ or apply the cherry-pick to TensorFlow 2.12 (commits 6fa05df and 915884f). 2. VALIDATE: Add explicit tensor rank checks before any call to array_ops.upper_bound — assert input.shape.rank == 2. 3. ISOLATE: Run TensorFlow serving processes with resource limits (ulimits, cgroups) to contain crash blast radius and enable auto-restart. 4. MONITOR: Alert on abnormal segfault/OOM kill rates in model serving containers — repeated crashes pattern may indicate active probing. 5. HARDEN: Deploy input schema validation (shape, dtype, rank) at API gateway layer before requests reach TensorFlow runtime.

What systems are affected by CVE-2023-33976?

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

What is the CVSS score for CVE-2023-33976?

CVE-2023-33976 has a CVSS v3.1 base score of 7.5 (HIGH). The EPSS exploitation probability is 0.04%.

Technical Details

NVD Description

TensorFlow is an end-to-end open source platform for machine learning. `array_ops.upper_bound` causes a segfault when not given a rank 2 tensor. The fix will be included in TensorFlow 2.13 and will also cherrypick this commit on TensorFlow 2.12.

Exploitation Scenario

An adversary targeting an organization's AI-powered product (e.g., an ML inference API for fraud detection or image classification) sends a crafted HTTP request to the model serving endpoint with a rank-1 tensor payload instead of the expected rank-2 input. TensorFlow's array_ops.upper_bound dereferences an invalid memory address, triggering a segfault that crashes the serving process. If the service auto-restarts, the adversary can loop requests to maintain a persistent DoS condition at negligible cost. No authentication, no ML knowledge, and no exploit code is needed — only a malformed tensor shape.