CVE-2022-36017 — HIGH (CVSS 7.5) AI Security Vulnerability

CISO Take

A network-exploitable denial-of-service in TensorFlow's Requantize op allows unauthenticated attackers to crash inference services by sending malformed tensor shapes — no auth, no special conditions. Any TF Serving deployment accepting external inputs is directly exposed. Patch immediately to TF 2.10.0, 2.9.1, 2.8.1, or 2.7.2; no workaround exists.

Risk Assessment

High risk for organizations running TensorFlow Serving or any TF inference endpoint exposed to untrusted input. CVSS 7.5 (AV:N/AC:L/PR:N/UI:N) means a single malformed request can crash the ML inference service with zero prerequisites. Impact is limited to availability (no C/I compromise), but repeated DoS renders AI-dependent services unreliable at scale. Internal-only deployments have reduced but non-zero risk if adversaries gain lateral network access.

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

6 steps

Patch to TF 2.10.0, TF 2.9.1, TF 2.8.1, or TF 2.7.2 (fix commit: 785d67a78a1d533759fcd2f5e8d6ef778de849e0).
No known workarounds — patching is the only remediation.
Add input validation at the API gateway layer to reject malformed or unexpected tensor ranks before reaching the model runtime.
Implement rate limiting and schema validation on TF Serving gRPC/HTTP endpoints.
Monitor inference service crash rates and pod restarts; alert on anomalous restart patterns.
Place TF Serving behind authenticated API gateways to reduce unauthenticated attack surface.

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

Clause 6.1 - Actions to address risks and opportunities

NIST AI RMF

GOVERN-1.7 - Processes for AI risk management MANAGE-2.2 - Mechanisms to sustain the value of deployed AI

Frequently Asked Questions

What is CVE-2022-36017?

A network-exploitable denial-of-service in TensorFlow's Requantize op allows unauthenticated attackers to crash inference services by sending malformed tensor shapes — no auth, no special conditions. Any TF Serving deployment accepting external inputs is directly exposed. Patch immediately to TF 2.10.0, 2.9.1, 2.8.1, or 2.7.2; no workaround exists.

Is CVE-2022-36017 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2022-36017, increasing the risk of exploitation.

How to fix CVE-2022-36017?

1. Patch to TF 2.10.0, TF 2.9.1, TF 2.8.1, or TF 2.7.2 (fix commit: 785d67a78a1d533759fcd2f5e8d6ef778de849e0). 2. No known workarounds — patching is the only remediation. 3. Add input validation at the API gateway layer to reject malformed or unexpected tensor ranks before reaching the model runtime. 4. Implement rate limiting and schema validation on TF Serving gRPC/HTTP endpoints. 5. Monitor inference service crash rates and pod restarts; alert on anomalous restart patterns. 6. Place TF Serving behind authenticated API gateways to reduce unauthenticated attack surface.

What systems are affected by CVE-2022-36017?

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

What is the CVSS score for CVE-2022-36017?

CVE-2022-36017 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. If `Requantize` is given `input_min`, `input_max`, `requested_output_min`, `requested_output_max` tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 785d67a78a1d533759fcd2f5e8d6ef778de849e0. 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's AI inference service discovers a TensorFlow Serving endpoint via port scan or API documentation. They craft a gRPC prediction request containing a Requantize operation where input_min/input_max/requested_output_min/requested_output_max are supplied as rank-1+ tensors instead of the expected scalar (rank-0) values. This triggers a segfault in the TF runtime, crashing the inference process. In a Kubernetes deployment, the pod restarts automatically — but repeated requests at low rate maintain a persistent DoS, disrupting real-time prediction APIs without triggering volumetric DDoS defenses.