CVE-2022-35981: TensorFlow DoS — HIGH

CISO Take

Any TensorFlow-based inference or training endpoint accepting user-controlled inputs is vulnerable to unauthenticated remote DoS. Patch immediately to TensorFlow 2.10.0, 2.9.1, 2.8.1, or 2.7.2 — no workaround exists since the crash occurs inside the TF kernel, bypassing application-layer input validation. Prioritize internet-facing model serving endpoints first.

What is the risk?

High severity (CVSS 7.5) with low exploitation complexity — no authentication or user interaction required over the network. An attacker with access to a TensorFlow serving endpoint can reliably crash it by sending malformed tensor dimensions to FractionalMaxPoolGrad. Not in CISA KEV and no known active exploitation, but the trivial exploit path combined with wide TensorFlow deployment in production ML systems makes this operationally significant for any organization running TF-based model serving at scale.

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 30% of all CVEs

Source: EPSS v3 — FIRST.org

Exploitation Status

Exploit Available

Exploitation: MEDIUM

Sophistication

Trivial

Exploitation Confidence

medium

○ 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

Upgrade TensorFlow to 2.10.0 (preferred), or cherrypick patch for 2.9.1, 2.8.1, 2.7.2.
Until patched, restrict network access to TF serving endpoints to trusted networks via firewall/security groups — this is the only effective workaround.
Deploy API gateway-level input shape validation to reject tensors with unexpected dimensions before reaching TF kernels.
Monitor for unexpected process crashes or container restarts in TF serving infrastructure as a detection signal.
Audit ML platform dependencies and pin TensorFlow versions in CI/CD pipelines to patched releases.

What does CISA's SSVC say?

Decision Track

Exploitation none

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.T0040 - AI Model Inference API Access 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

ISO 42001

A.9.3 - AI system operation and monitoring

NIST AI RMF

MANAGE 2.2 - Mechanisms are in place to inventory AI risks and manage their treatment

OWASP LLM Top 10

LLM04 - Model Denial of Service

Frequently Asked Questions

What is CVE-2022-35981?

Any TensorFlow-based inference or training endpoint accepting user-controlled inputs is vulnerable to unauthenticated remote DoS. Patch immediately to TensorFlow 2.10.0, 2.9.1, 2.8.1, or 2.7.2 — no workaround exists since the crash occurs inside the TF kernel, bypassing application-layer input validation. Prioritize internet-facing model serving endpoints first.

Is CVE-2022-35981 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2022-35981, increasing the risk of exploitation.

How to fix CVE-2022-35981?

1. Upgrade TensorFlow to 2.10.0 (preferred), or cherrypick patch for 2.9.1, 2.8.1, 2.7.2. 2. Until patched, restrict network access to TF serving endpoints to trusted networks via firewall/security groups — this is the only effective workaround. 3. Deploy API gateway-level input shape validation to reject tensors with unexpected dimensions before reaching TF kernels. 4. Monitor for unexpected process crashes or container restarts in TF serving infrastructure as a detection signal. 5. Audit ML platform dependencies and pin TensorFlow versions in CI/CD pipelines to patched releases.

What systems are affected by CVE-2022-35981?

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

What is the CVSS score for CVE-2022-35981?

CVE-2022-35981 has a CVSS v3.1 base score of 7.5 (HIGH). The EPSS exploitation probability is 0.38%.

What is the AI security impact?

Affected AI Architectures

model servingtraining pipelinesinference endpoints

MITRE ATLAS Techniques

AML.T0029 Denial of AI Service

AML.T0040 AI Model Inference API Access

AML.T0049 Exploit Public-Facing Application

Compliance Controls Affected

EU AI Act: Article 15

ISO 42001: A.9.3

NIST AI RMF: MANAGE 2.2

OWASP LLM Top 10: LLM04

What are the technical details?

Original Advisory

TensorFlow is an open source platform for machine learning. `FractionalMaxPoolGrad` validates its inputs with `CHECK` failures instead of with returning errors. If it gets incorrectly sized inputs, the `CHECK` failure can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 8741e57d163a079db05a7107a7609af70931def4. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

Exploitation Scenario

An adversary targeting a company's TensorFlow-based image classification inference API sends a crafted HTTP request with a tensor containing incorrect dimensions to the FractionalMaxPoolGrad operation. The operation triggers a CHECK assertion failure, causing the TF process to abort immediately. In a multi-tenant MLaaS environment, a malicious tenant could crash shared serving infrastructure affecting all other customers. The attack is trivially repeatable with no authentication required — a simple loop of malformed requests creates a sustained DoS against the ML endpoint.

Weaknesses (CWE)

CWE-617 Reachable Assertion

CWE-617 — Reachable Assertion: The product contains an assert() or similar statement that can be triggered by an attacker, which leads to an application exit or other behavior that is more severe than necessary.

[Implementation] Make sensitive open/close operation non reachable by directly user-controlled data (e.g. open/close resources)
[Implementation] Perform input validation on user data.

Source: MITRE CWE corpus.