CVE-2022-35964: TensorFlow remote DoS

CISO Take

Any TensorFlow deployment running versions prior to 2.10.0/2.9.1/2.8.1/2.7.2 that exposes LSTM gradient operations over a network is vulnerable to unauthenticated remote crash. Patch immediately to the fixed versions; no workaround exists. Prioritize training infrastructure and model-serving endpoints that accept user-controlled tensor inputs.

What is the risk?

CVSS 7.5 with AV:N/AC:L/PR:N/UI:N makes this trivially exploitable by any network-adjacent attacker with no authentication. The impact is confined to availability (no confidentiality or integrity loss), but repeated exploitation can permanently disrupt training jobs or inference services. Exposure is high in organizations using TF-Serving or Kubeflow pipelines with externally reachable endpoints.

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 31% 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

Patch: upgrade to TensorFlow 2.10.0, 2.9.1, 2.8.1, or 2.7.2 immediately.
Network isolation: restrict TF Serving and training API endpoints to internal networks; do not expose raw TF op execution to untrusted clients.
Input validation: add upstream schema validation to reject malformed tensor shapes before they reach TF ops.
Detection: monitor for abnormal process crashes or pod restarts in ML infrastructure; alert on TF worker SIGSEGV signals.
No workaround exists per vendor advisory—patching is the only remediation.

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 Training Data Inference AML.T0010.001 - AI Software AML.T0029 - Denial of AI Service AML.T0043 - Craft Adversarial Data AML.T0049 - Exploit Public-Facing Application

Which compliance frameworks are affected?

This CVE is relevant to:

EU AI Act

Art. 15 - Accuracy, robustness and cybersecurity

ISO 42001

A.9.3 - AI system availability and resilience

NIST AI RMF

GOVERN 6.1 - Policies for AI risk and vulnerability management MANAGE 2.2 - Mechanisms to sustain AI risk management

Frequently Asked Questions

What is CVE-2022-35964?

Any TensorFlow deployment running versions prior to 2.10.0/2.9.1/2.8.1/2.7.2 that exposes LSTM gradient operations over a network is vulnerable to unauthenticated remote crash. Patch immediately to the fixed versions; no workaround exists. Prioritize training infrastructure and model-serving endpoints that accept user-controlled tensor inputs.

Is CVE-2022-35964 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2022-35964, increasing the risk of exploitation.

How to fix CVE-2022-35964?

1. Patch: upgrade to TensorFlow 2.10.0, 2.9.1, 2.8.1, or 2.7.2 immediately. 2. Network isolation: restrict TF Serving and training API endpoints to internal networks; do not expose raw TF op execution to untrusted clients. 3. Input validation: add upstream schema validation to reject malformed tensor shapes before they reach TF ops. 4. Detection: monitor for abnormal process crashes or pod restarts in ML infrastructure; alert on TF worker SIGSEGV signals. 5. No workaround exists per vendor advisory—patching is the only remediation.

What systems are affected by CVE-2022-35964?

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

What is the CVSS score for CVE-2022-35964?

CVE-2022-35964 has a CVSS v3.1 base score of 7.5 (HIGH). The EPSS exploitation probability is 0.39%.

What is the AI security impact?

Affected AI Architectures

training pipelinesmodel servingML infrastructure

MITRE ATLAS Techniques

AML.T0010.001 AI Software

AML.T0029 Denial of AI Service

AML.T0043 Craft Adversarial Data

AML.T0049 Exploit Public-Facing Application

Compliance Controls Affected

EU AI Act: Art. 15

ISO 42001: A.9.3

NIST AI RMF: GOVERN 6.1, MANAGE 2.2

What are the technical details?

Original Advisory

TensorFlow is an open source platform for machine learning. The implementation of `BlockLSTMGradV2` does not fully validate its inputs. This results in a a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 2a458fc4866505be27c62f81474ecb2b870498fa. 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 identifies a publicly reachable TensorFlow Serving endpoint or an internal MLOps API (e.g., Kubeflow pipeline trigger) that internally invokes LSTM gradient operations. They craft a malformed tensor input with unexpected shape or dtype for the BlockLSTMGradV2 kernel—no authentication or AI/ML expertise required, just knowledge of TF op signatures (publicly documented). The malformed input bypasses TF's incomplete input validation and triggers a segfault, killing the TF process. In a training cluster, this aborts long-running jobs. In a serving context, it takes the inference endpoint offline. Automated retries with the same payload produce a sustained DoS.

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.