CVE-2022-41888: TensorFlow GPU input validation DoS

CISO Take

A missing rank validation in TensorFlow's GPU-accelerated bounding box operation allows unauthenticated remote attackers to crash any inference service exposing this functionality. If your computer vision or object detection pipelines run on GPU-backed TensorFlow Serving endpoints, this is a live DoS vector. Patch immediately to TF 2.11, 2.10.1, 2.9.3, or 2.8.4.

What is the risk?

CVSS 7.5 with network vector, low complexity, no authentication required makes this trivially exploitable against exposed endpoints. Impact is limited to availability — no data exfiltration or code execution path identified. Real-world risk is elevated for AI infrastructure teams running GPU-accelerated object detection or autonomous systems pipelines behind internal or external APIs. The lack of CISA KEV listing and no known active exploitation reduces urgency slightly, but the low barrier to exploit justifies prompt patching.

What systems are affected?

Package	Ecosystem	Vulnerable Range	Patched
TensorFlow	pip	—	No patch
195.8K OpenSSF 7.1 3.7K dependents Pushed 3d ago 4% patched ~1372d to patch Full package profile →

Do you use TensorFlow? You're affected.

How severe is it?

CVSS 3.1

7.5 / 10

EPSS

0.4%

chance of exploitation in 30 days

Higher than 35% of all CVEs

Source: EPSS v3 — FIRST.org

Exploitation Status

Exploit Available

Exploitation: MEDIUM

Sophistication

Trivial

Exploitation Confidence

medium

○ CISA SSVC: Public PoC

○ Public PoC indexed (trickest/cve)

Composite signal derived from CISA KEV, VulnCheck KEV, CISA SSVC, EPSS, Metasploit, Exploit-DB, trickest/cve, Nuclei templates, and inthewild.io exploitation reports.

What is the attack surface?

AV Network

AC Low

PR None

UI None

S Unchanged

C None

I None

A High

What should I do?

5 steps

PATCH

Upgrade to TensorFlow 2.11, 2.10.1, 2.9.3, or 2.8.4 — all contain commit cf35502.
WORKAROUND

Add explicit rank assertion before passing scores tensor to the op (e.g., tf.debugging.assert_rank(scores, 4)).
INPUT VALIDATION

Enforce tensor shape validation at API gateway or gRPC interceptor layer before requests reach the model server.
NETWORK HARDENING

Ensure TF Serving gRPC (port 8500) and REST (port 8501) endpoints are not internet-facing without authentication.
DETECTION

Monitor for repeated inference service crashes or GPU process restarts as a potential exploitation indicator.

What does CISA's SSVC say?

Decision Track*

Exploitation poc

Automatable No

Technical Impact partial

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

How is it classified?

DoS Framework Inference AML.T0029 - Denial of AI Service AML.T0046 - Spamming AI System with Chaff Data AML.T0049 - Exploit Public-Facing Application

Which compliance frameworks are affected?

This CVE is relevant to:

EU AI Act

Article 15 - Accuracy, robustness and cybersecurity for high-risk AI systems

ISO 42001

8.4 - AI System Operation

NIST AI RMF

GOVERN 6.2 - Policies and procedures are in place for AI risk management MANAGE 2.2 - Mechanisms to achieve AI risk management objectives are planned and in place

Frequently Asked Questions

What is CVE-2022-41888?

A missing rank validation in TensorFlow's GPU-accelerated bounding box operation allows unauthenticated remote attackers to crash any inference service exposing this functionality. If your computer vision or object detection pipelines run on GPU-backed TensorFlow Serving endpoints, this is a live DoS vector. Patch immediately to TF 2.11, 2.10.1, 2.9.3, or 2.8.4.

Is CVE-2022-41888 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2022-41888, increasing the risk of exploitation.

How to fix CVE-2022-41888?

1. PATCH: Upgrade to TensorFlow 2.11, 2.10.1, 2.9.3, or 2.8.4 — all contain commit cf35502. 2. WORKAROUND: Add explicit rank assertion before passing scores tensor to the op (e.g., tf.debugging.assert_rank(scores, 4)). 3. INPUT VALIDATION: Enforce tensor shape validation at API gateway or gRPC interceptor layer before requests reach the model server. 4. NETWORK HARDENING: Ensure TF Serving gRPC (port 8500) and REST (port 8501) endpoints are not internet-facing without authentication. 5. DETECTION: Monitor for repeated inference service crashes or GPU process restarts as a potential exploitation indicator.

What systems are affected by CVE-2022-41888?

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

What is the CVSS score for CVE-2022-41888?

CVE-2022-41888 has a CVSS v3.1 base score of 7.5 (HIGH). The EPSS exploitation probability is 0.44%.

What is the AI security impact?

Affected AI Architectures

model servingtraining pipelinesinference

MITRE ATLAS Techniques

AML.T0029 Denial of AI Service

AML.T0046 Spamming AI System with Chaff Data

AML.T0049 Exploit Public-Facing Application

Compliance Controls Affected

EU AI Act: Article 15

ISO 42001: 8.4

NIST AI RMF: GOVERN 6.2, MANAGE 2.2

What are the technical details?

Original Advisory

TensorFlow is an open source platform for machine learning. When running on GPU, `tf.image.generate_bounding_box_proposals` receives a `scores` input that must be of rank 4 but is not checked. We have patched the issue in GitHub commit cf35502463a88ca7185a99daa7031df60b3c1c98. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

Exploitation Scenario

An adversary targeting an organization's computer vision API (e.g., an object detection service for security camera analysis or medical imaging) identifies that the backend uses TensorFlow on GPU. They craft a malformed gRPC or REST inference request to the model's predict endpoint, supplying a scores tensor with rank 3 instead of the required 4. The missing input validation causes the CUDA kernel to receive an unexpected tensor shape, triggering a crash of the GPU-side process. The attacker automates this request at intervals to maintain a persistent denial of service against the inference endpoint, disrupting downstream automated decision systems that depend on the model's output.

Weaknesses (CWE)

CWE-20 Improper Input Validation

CWE-20 — Improper Input Validation: The product receives input or data, but it does not validate or incorrectly validates that the input has the properties that are required to process the data safely and correctly.

[Architecture and Design] Consider using language-theoretic security (LangSec) techniques that characterize inputs using a formal language and build "recognizers" for that language. This effectively requires parsing to be a distinct layer that effectively enforces a boundary between raw input and internal data representations, instead of allowing parser code to be scattered throughout the program, where it could be subject to errors or inconsistencies that create weaknesses. [REF-1109] [REF-1110] [REF-1111]
[Architecture and Design] Use an input validation framework such as Struts or the OWASP ESAPI Validation API. Note that using a framework does not automatically address all input validation problems; be mindful of weaknesses that could arise from misusing the framework itself (CWE-1173).

Source: MITRE CWE corpus.