CVE-2022-35960 — HIGH (CVSS 7.5) AI Security Vulnerability

CISO Take

Any TensorFlow deployment exposing raw ops endpoints—including TF Serving instances—is vulnerable to unauthenticated process crashes via a single malformed request. Patch immediately to TensorFlow 2.10.0, 2.9.1, 2.8.1, or 2.7.2; no workaround exists. Availability impact only, but a downed inference service is a downed business function.

Risk Assessment

CVSS 7.5 with network-accessible attack vector, zero authentication required, and low complexity makes this trivially exploitable by any attacker who can reach a TF endpoint. The blast radius is limited to availability (no data exfiltration or code execution), but in production ML inference pipelines this means complete service disruption. Not in CISA KEV and patched since 2022, so residual risk exists only in unpatched deployments.

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

0.2%

chance of exploitation in 30 days

Higher than 43% 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, 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

Upgrade to TensorFlow 2.10.0, 2.9.1, 2.8.1, or 2.7.2 (commit b5f6fbf).
No workaround available per upstream advisory—patching is mandatory.
As interim hardening, place input validation/sanitization middleware in front of TF Serving to reject tensors with unexpected shapes before they reach op execution.
Monitor for sudden TF process crashes or repeated inference service restarts as exploitation indicators.
Audit all TF Serving deployments for version; prioritize internet-exposed or multi-tenant instances.

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.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 9 - Risk Management System

ISO 42001

A.6.2.4 - AI System Security and Robustness

NIST AI RMF

GOVERN-1.7 - Processes for AI risk monitoring and incident response MANAGE-2.2 - Mechanisms to respond to AI risks

Frequently Asked Questions

What is CVE-2022-35960?

Any TensorFlow deployment exposing raw ops endpoints—including TF Serving instances—is vulnerable to unauthenticated process crashes via a single malformed request. Patch immediately to TensorFlow 2.10.0, 2.9.1, 2.8.1, or 2.7.2; no workaround exists. Availability impact only, but a downed inference service is a downed business function.

Is CVE-2022-35960 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2022-35960, increasing the risk of exploitation.

How to fix CVE-2022-35960?

1. Upgrade to TensorFlow 2.10.0, 2.9.1, 2.8.1, or 2.7.2 (commit b5f6fbf). 2. No workaround available per upstream advisory—patching is mandatory. 3. As interim hardening, place input validation/sanitization middleware in front of TF Serving to reject tensors with unexpected shapes before they reach op execution. 4. Monitor for sudden TF process crashes or repeated inference service restarts as exploitation indicators. 5. Audit all TF Serving deployments for version; prioritize internet-exposed or multi-tenant instances.

What systems are affected by CVE-2022-35960?

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

What is the CVSS score for CVE-2022-35960?

CVE-2022-35960 has a CVSS v3.1 base score of 7.5 (HIGH). The EPSS exploitation probability is 0.21%.

Technical Details

NVD Description

TensorFlow is an open source platform for machine learning. In `core/kernels/list_kernels.cc's TensorListReserve`, `num_elements` is assumed to be a tensor of size 1. When a `num_elements` of more than 1 element is provided, then `tf.raw_ops.TensorListReserve` fails the `CHECK_EQ` in `CheckIsAlignedAndSingleElement`. We have patched the issue in GitHub commit b5f6fbfba76576202b72119897561e3bd4f179c7. 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 attacker identifies a TensorFlow Serving endpoint via active scanning or public cloud enumeration. They craft a gRPC or REST request calling `tf.raw_ops.TensorListReserve` with a `num_elements` tensor containing more than one element—a trivially constructed malformed payload. The assertion `CHECK_EQ` fires in `CheckIsAlignedAndSingleElement`, crashing the TensorFlow process immediately. In a Kubernetes deployment without proper restart policies and health checks, the inference service goes offline. Repeated requests keep it in a crash loop, achieving sustained denial of service against the ML inference backend.