CVE-2022-35990 — HIGH (CVSS 7.5) AI Security Vulnerability

CISO Take

A remotely exploitable, unauthenticated DoS exists in TensorFlow's quantization-aware training gradient op — a malformed input tensor rank triggers a fatal assertion, crashing the process. If you expose TensorFlow serving or training endpoints to untrusted or user-supplied inputs, patch immediately to TF 2.10.0, 2.9.1, 2.8.1, or 2.7.2. No workaround exists; the fix is patching.

Risk Assessment

CVSS 7.5 High with network-accessible, zero-auth, low-complexity exploitation raises the operational bar significantly for AI/ML teams running TF-based inference APIs. The DoS-only impact (no data exfiltration or code execution) tempers urgency, and the specific op affected (fake_quant_with_min_max_vars_per_channel_gradient) is more common in QAT training pipelines than pure inference deployments. Real-world risk is highest for orgs exposing MLOps platforms, notebook servers, or training APIs to multi-tenant or internet-facing environments. Not in CISA KEV; no known active exploitation.

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 20% 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 TensorFlow 2.10.0, or apply cherrypicks to 2.9.1, 2.8.1, or 2.7.2. No other workaround exists per upstream.
Defense-in-depth: Validate tensor rank (must be 1) for min/max inputs before reaching fake_quant ops — add input schema validation at API layer.
Network controls: Restrict access to TF Serving and training endpoints to authorized clients only; do not expose to public internet without authentication.
Detection: Monitor TF process logs for CHECK failure stack traces referencing fake_quant_with_min_max_vars_per_channel_gradient; alert on unexpected process restarts in serving containers.
Inventory: Identify all TF versions in your ML stack — include transitive dependencies in MLOps tooling (notebooks, AutoML platforms).

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 15 - Accuracy, robustness and cybersecurity

ISO 42001

8.4 - AI system operation and monitoring

NIST AI RMF

MANAGE 2.2 - Mechanisms to sustain AI system operation

OWASP LLM Top 10

LLM04 - Model Denial of Service

Frequently Asked Questions

What is CVE-2022-35990?

A remotely exploitable, unauthenticated DoS exists in TensorFlow's quantization-aware training gradient op — a malformed input tensor rank triggers a fatal assertion, crashing the process. If you expose TensorFlow serving or training endpoints to untrusted or user-supplied inputs, patch immediately to TF 2.10.0, 2.9.1, 2.8.1, or 2.7.2. No workaround exists; the fix is patching.

Is CVE-2022-35990 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2022-35990, increasing the risk of exploitation.

How to fix CVE-2022-35990?

1. Patch: Upgrade to TensorFlow 2.10.0, or apply cherrypicks to 2.9.1, 2.8.1, or 2.7.2. No other workaround exists per upstream. 2. Defense-in-depth: Validate tensor rank (must be 1) for min/max inputs before reaching fake_quant ops — add input schema validation at API layer. 3. Network controls: Restrict access to TF Serving and training endpoints to authorized clients only; do not expose to public internet without authentication. 4. Detection: Monitor TF process logs for CHECK failure stack traces referencing fake_quant_with_min_max_vars_per_channel_gradient; alert on unexpected process restarts in serving containers. 5. Inventory: Identify all TF versions in your ML stack — include transitive dependencies in MLOps tooling (notebooks, AutoML platforms).

What systems are affected by CVE-2022-35990?

This vulnerability affects the following AI/ML architecture patterns: training pipelines, model serving, MLOps platforms, edge/mobile model optimization.

What is the CVSS score for CVE-2022-35990?

CVE-2022-35990 has a CVSS v3.1 base score of 7.5 (HIGH). The EPSS exploitation probability is 0.06%.

Technical Details

NVD Description

TensorFlow is an open source platform for machine learning. When `tf.quantization.fake_quant_with_min_max_vars_per_channel_gradient` receives input `min` or `max` of rank other than 1, it gives a `CHECK` fail that can trigger a denial of service attack. We have patched the issue in GitHub commit f3cf67ac5705f4f04721d15e485e192bb319feed. 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 an organization running a quantized TensorFlow model behind an inference API sends a crafted POST request to the /v1/models/:predict endpoint, supplying a min or max parameter as a rank-2 (or higher) tensor instead of the expected rank-1 vector. TensorFlow's internal CHECK assertion fires immediately, raising a SIGABRT that kills the serving worker. In a Kubernetes-based deployment this triggers a CrashLoopBackOff, effectively taking the inference endpoint offline. The attack requires no authentication, no ML expertise beyond reading TF API documentation, and can be automated to maintain a persistent DoS condition against the targeted AI service.