CVE-2022-23577 — MEDIUM (CVSS 6.5) AI Security Vulnerability

CISO Take

A low-privilege network attacker can crash TensorFlow serving infrastructure by triggering a null pointer dereference in the SavedModel loader's GetInitOp function. This is a denial-of-service risk for any internet-facing or internally-exposed TensorFlow inference endpoint. Patch immediately to TF 2.8.0, 2.7.1, 2.6.3, or 2.5.3; restrict serving endpoint access to trusted principals in the interim.

Risk Assessment

Medium risk overall, but operationally significant for organizations running TensorFlow model serving at scale. CVSS 6.5 with network vector and low privileges required makes it trivially exploitable by any authenticated user or compromised service account. Impact is limited to availability (no data exfiltration or code execution possible), but repeated crashes in production inference pipelines could cause meaningful business disruption. Not in CISA KEV and 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

6.5 / 10

EPSS

0.2%

chance of exploitation in 30 days

Higher than 44% of all CVEs

Source: EPSS v3 — FIRST.org

Exploitation Status

Exploit Available

Exploitation: MEDIUM

Sophistication

Trivial

Exploitation Confidence

medium

○ CISA SSVC: Public PoC

○ 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 Low

UI None

S Unchanged

C None

I None

A High

Recommended Action

5 steps

Patch: Upgrade TensorFlow to 2.8.0, or apply cherry-pick backports to 2.7.1, 2.6.3, or 2.5.3.
Network hardening: Place TensorFlow serving endpoints behind authenticated API gateways; do not expose raw TF Serving ports to untrusted networks.
Process isolation: Run TF Serving workers in containers with auto-restart policies so crashes auto-recover (reduces blast radius while patching).
Detection: Alert on abnormal crash/restart rates in model serving containers; log null pointer/segfault signals from TF processes.
If running on unpatched versions, consider disabling dynamic SavedModel loading from untrusted sources.

CISA SSVC Assessment

Decision Track*

Exploitation poc

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

9.1 - Monitoring, measurement, analysis and evaluation

NIST AI RMF

MANAGE 2.4 - Residual risks are managed

OWASP LLM Top 10

LLM09 - Overreliance

Frequently Asked Questions

What is CVE-2022-23577?

A low-privilege network attacker can crash TensorFlow serving infrastructure by triggering a null pointer dereference in the SavedModel loader's GetInitOp function. This is a denial-of-service risk for any internet-facing or internally-exposed TensorFlow inference endpoint. Patch immediately to TF 2.8.0, 2.7.1, 2.6.3, or 2.5.3; restrict serving endpoint access to trusted principals in the interim.

Is CVE-2022-23577 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2022-23577, increasing the risk of exploitation.

How to fix CVE-2022-23577?

1. Patch: Upgrade TensorFlow to 2.8.0, or apply cherry-pick backports to 2.7.1, 2.6.3, or 2.5.3. 2. Network hardening: Place TensorFlow serving endpoints behind authenticated API gateways; do not expose raw TF Serving ports to untrusted networks. 3. Process isolation: Run TF Serving workers in containers with auto-restart policies so crashes auto-recover (reduces blast radius while patching). 4. Detection: Alert on abnormal crash/restart rates in model serving containers; log null pointer/segfault signals from TF processes. 5. If running on unpatched versions, consider disabling dynamic SavedModel loading from untrusted sources.

What systems are affected by CVE-2022-23577?

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

What is the CVSS score for CVE-2022-23577?

CVE-2022-23577 has a CVSS v3.1 base score of 6.5 (MEDIUM). The EPSS exploitation probability is 0.22%.

Technical Details

NVD Description

Tensorflow is an Open Source Machine Learning Framework. The implementation of `GetInitOp` is vulnerable to a crash caused by dereferencing a null pointer. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Exploitation Scenario

An adversary with low-privilege access (e.g., a data scientist account, a compromised CI/CD service account, or a legitimate but malicious user in a shared ML platform) submits a crafted SavedModel or triggers an inference request that causes GetInitOp to dereference a null pointer. The TensorFlow serving process crashes immediately with no error recovery. In a production environment without auto-restart, this takes the inference endpoint offline. An adversary could script repeated crash triggers to maintain a persistent denial-of-service condition against a competitor's shared ML inference cluster or a SaaS AI product.