CVE-2022-35986 — HIGH (CVSS 7.5) AI Security Vulnerability

CISO Take

Any TensorFlow inference endpoint processing ragged tensor inputs is vulnerable to unauthenticated remote crash via a single malformed request — no auth, no complexity. Patch to TF 2.10.0/2.9.1/2.8.1/2.7.2 immediately and put API gateway input validation in front of TF Serving. Internal-only training workloads are lower priority but still exposed if reachable by untrusted users.

Risk Assessment

CVSS 7.5 HIGH with AV:N/AC:L/PR:N/UI:N is a reliable, trivial unauthenticated DoS. Impact is limited to availability — no data exfiltration or code execution path. Risk is highest for organizations exposing TensorFlow Serving or custom TF inference APIs on untrusted networks without input validation or API gateway protection. Without HA/auto-restart, a single request takes the inference service offline.

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 21% 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

PATCH

Upgrade to TF 2.10.0, 2.9.1, 2.8.1, or 2.7.2 — no workaround exists per vendor advisory.
INPUT VALIDATION

Add API gateway or application-layer checks to reject empty or malformed tensor shapes before they reach TF ops.
PROCESS ISOLATION

Run TF Serving in containerized processes with auto-restart policies (systemd restart=always, k8s liveness probe) to minimize availability impact.
NETWORK CONTROLS

Restrict TF Serving ports (8500/8501) to internal networks; never expose directly to internet.
DETECTION

Alert on unexpected TF Serving process restarts or segfaults in application logs — a pattern of crashes correlating with specific request origins indicates active exploitation.

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.T0029 - Denial of AI Service AML.T0040 - AI Model Inference API Access 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.10.1 - AI system operation and monitoring

NIST AI RMF

MANAGE 2.2 - Mechanisms to start, stop, and recover AI system operation

OWASP LLM Top 10

LLM04 - Model Denial of Service

Frequently Asked Questions

What is CVE-2022-35986?

Any TensorFlow inference endpoint processing ragged tensor inputs is vulnerable to unauthenticated remote crash via a single malformed request — no auth, no complexity. Patch to TF 2.10.0/2.9.1/2.8.1/2.7.2 immediately and put API gateway input validation in front of TF Serving. Internal-only training workloads are lower priority but still exposed if reachable by untrusted users.

Is CVE-2022-35986 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2022-35986, increasing the risk of exploitation.

How to fix CVE-2022-35986?

1. PATCH: Upgrade to TF 2.10.0, 2.9.1, 2.8.1, or 2.7.2 — no workaround exists per vendor advisory. 2. INPUT VALIDATION: Add API gateway or application-layer checks to reject empty or malformed tensor shapes before they reach TF ops. 3. PROCESS ISOLATION: Run TF Serving in containerized processes with auto-restart policies (systemd restart=always, k8s liveness probe) to minimize availability impact. 4. NETWORK CONTROLS: Restrict TF Serving ports (8500/8501) to internal networks; never expose directly to internet. 5. DETECTION: Alert on unexpected TF Serving process restarts or segfaults in application logs — a pattern of crashes correlating with specific request origins indicates active exploitation.

What systems are affected by CVE-2022-35986?

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

What is the CVSS score for CVE-2022-35986?

CVE-2022-35986 has a CVSS v3.1 base score of 7.5 (HIGH). The EPSS exploitation probability is 0.07%.

Technical Details

NVD Description

TensorFlow is an open source platform for machine learning. If `RaggedBincount` is given an empty input tensor `splits`, it results in a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 7a4591fd4f065f4fa903593bc39b2f79530a74b8. 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

Adversary identifies a public-facing NLP classification API built on TensorFlow Serving. Via passive recon (job postings, API error messages, model metadata endpoints), confirms TF backend. Sends a crafted gRPC or REST inference request containing a RaggedBincount op with an empty splits tensor — this is a trivial payload requiring no specialized ML knowledge. The TF Serving process segfaults and crashes. In a single-replica deployment, the AI service is fully offline. The attacker loops the request to prevent recovery if auto-restart is in place. No credentials, no prior access, no user interaction required.