CVE-2022-41907 — HIGH (CVSS 7.5) AI Security Vulnerability

CISO Take

An unauthenticated remote attacker can crash any TensorFlow inference service by sending a crafted image resize request with an oversized `size` parameter, triggering an integer overflow. If your ML serving infrastructure exposes TensorFlow endpoints publicly or accepts untrusted input, this is an availability risk requiring immediate patching. Upgrade to TF 2.11, 2.10.1, 2.9.3, or 2.8.4 — no workaround exists short of input validation at the API boundary.

Risk Assessment

High severity for publicly exposed TF serving endpoints due to network-exploitable, zero-auth, zero-interaction exploit path (CVSS 7.5). Impact is limited to availability — no data exfiltration or integrity compromise. Risk is elevated in production computer vision APIs where arbitrary image dimensions are accepted. Reduced risk for air-gapped training environments or pipelines with strict input schema validation. Not in CISA KEV and no evidence of active exploitation, keeping effective risk at MEDIUM-HIGH for most organizations.

Affected Systems

Package	Ecosystem	Vulnerable Range	Patched
tensorflow	pip	—	No patch
195.0K OpenSSF 7.2 3.7K dependents Pushed today 4% patched ~1372d to patch Full package profile →

Do you use tensorflow? You're affected.

Severity & Risk

CVSS 3.1

7.5 / 10

EPSS

0.1%

chance of exploitation in 30 days

Higher than 34% 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, CISA SSVC, EPSS, trickest/cve, and Nuclei templates.

Attack Surface

AV Network

AC Low

PR None

UI None

S Unchanged

C None

I None

A High

Recommended Action

5 steps

PATCH

Upgrade TensorFlow to 2.11.0+, 2.10.1+, 2.9.3+, or 2.8.4+ (commit 00c821af).
VALIDATE

Enforce strict input validation on image dimensions at API boundaries — reject or clamp size values above a safe maximum before reaching TF ops.
ISOLATE

Run TF Serving behind an API gateway with request schema validation (e.g., max height/width constraints).
DETECT

Monitor for sudden process crashes or OOM kills in TF serving processes — repeated crashes from a single source may indicate exploitation attempts.
VERIFY

Check pip show tensorflow in all serving environments; scan Dockerfiles and requirements.txt for pinned pre-patch versions.

CISA SSVC Assessment

Decision Track*

Exploitation poc

Automatable No

Technical Impact partial

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

Classification

DoS 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

Article 15 - Accuracy, robustness and cybersecurity

ISO 42001

A.6.2 - AI system operation and monitoring

NIST AI RMF

MANAGE 2.4 - Residual risks from AI system vulnerabilities

Frequently Asked Questions

What is CVE-2022-41907?

An unauthenticated remote attacker can crash any TensorFlow inference service by sending a crafted image resize request with an oversized `size` parameter, triggering an integer overflow. If your ML serving infrastructure exposes TensorFlow endpoints publicly or accepts untrusted input, this is an availability risk requiring immediate patching. Upgrade to TF 2.11, 2.10.1, 2.9.3, or 2.8.4 — no workaround exists short of input validation at the API boundary.

Is CVE-2022-41907 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2022-41907, increasing the risk of exploitation.

How to fix CVE-2022-41907?

1. PATCH: Upgrade TensorFlow to 2.11.0+, 2.10.1+, 2.9.3+, or 2.8.4+ (commit 00c821af). 2. VALIDATE: Enforce strict input validation on image dimensions at API boundaries — reject or clamp `size` values above a safe maximum before reaching TF ops. 3. ISOLATE: Run TF Serving behind an API gateway with request schema validation (e.g., max height/width constraints). 4. DETECT: Monitor for sudden process crashes or OOM kills in TF serving processes — repeated crashes from a single source may indicate exploitation attempts. 5. VERIFY: Check `pip show tensorflow` in all serving environments; scan Dockerfiles and requirements.txt for pinned pre-patch versions.

What systems are affected by CVE-2022-41907?

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

What is the CVSS score for CVE-2022-41907?

CVE-2022-41907 has a CVSS v3.1 base score of 7.5 (HIGH). The EPSS exploitation probability is 0.14%.

Technical Details

NVD Description

TensorFlow is an open source platform for machine learning. When `tf.raw_ops.ResizeNearestNeighborGrad` is given a large `size` input, it overflows. We have patched the issue in GitHub commit 00c821af032ba9e5f5fa3fe14690c8d28a657624. 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 a computer vision API (e.g., an image classification or object detection endpoint) sends a crafted inference request containing an image with an extreme `size` parameter to trigger the `ResizeNearestNeighborGrad` operation. The integer overflow occurs server-side, causing the TF runtime to crash. This requires no credentials, no special AI/ML knowledge, and no prior access — only the ability to send a network request. Automated scanners could discover exposed TF Serving endpoints (default port 8501) and exploit this at scale to degrade availability of AI-powered services.