CVE-2022-41891 — HIGH (CVSS 7.5) AI Security Vulnerability

Q: Is CVE-2022-41891 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2022-41891, increasing the risk of exploitation.

Q: How to fix CVE-2022-41891?

1. Patch: Upgrade to TensorFlow 2.11, 2.10.1, 2.9.3, or 2.8.4 — cherry-pick commit fc33f3dc4c14051a83eec6535b608abe1d355fde if running a custom build. 2. Short-term workaround: Add input validation middleware to inference endpoints that rejects requests with empty element_shape tensors before they reach the TF runtime. 3. Network hardening: Ensure TF Serving is not directly internet-exposed; place behind an API gateway or load balancer that performs basic schema validation. 4. Detection: Monitor for abnormal process restarts or segfault signals in TF Serving logs; alert on SIGSEGV in inference worker processes. 5. Verify: Run `python -c "import tensorflow as tf; print(tf.__version__)"` across all inference nodes to confirm patched versions.

Q: What systems are affected by CVE-2022-41891?

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

Q: What is the CVSS score for CVE-2022-41891?

CVE-2022-41891 has a CVSS v3.1 base score of 7.5 (HIGH). The EPSS exploitation probability is 0.18%.

CISO Take

Any TensorFlow-based model serving endpoint is remotely crashable with a single malformed request — no authentication required. If your inference infrastructure runs TF < 2.11/2.10.1/2.9.3/2.8.4 and is network-accessible, patch immediately. This is a trivial availability kill-switch for production ML pipelines.

Risk Assessment

High risk for organizations with network-exposed TensorFlow serving endpoints. CVSS 7.5 with AV:N/AC:L/PR:N/UI:N means unauthenticated remote exploitation with near-zero complexity — essentially a one-liner crash trigger. The blast radius is limited to availability (no C/I impact), but in production AI inference contexts, a DoS against model serving equates to direct business disruption. Risk is elevated because TF Serving is commonly exposed internally across large ML platforms without input validation layers.

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 39% 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 None

UI None

S Unchanged

C None

I None

A High

Recommended Action

5 steps

Patch: Upgrade to TensorFlow 2.11, 2.10.1, 2.9.3, or 2.8.4 — cherry-pick commit fc33f3dc4c14051a83eec6535b608abe1d355fde if running a custom build.
Short-term workaround: Add input validation middleware to inference endpoints that rejects requests with empty element_shape tensors before they reach the TF runtime.
Network hardening: Ensure TF Serving is not directly internet-exposed; place behind an API gateway or load balancer that performs basic schema validation.
Detection: Monitor for abnormal process restarts or segfault signals in TF Serving logs; alert on SIGSEGV in inference worker processes.
Verify: Run python -c "import tensorflow as tf; print(tf.__version__)" across all inference nodes to confirm patched versions.

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

Art.15 - Accuracy, robustness and cybersecurity for high-risk AI systems

ISO 42001

A.6.2.6 - AI system security and resilience

NIST AI RMF

GOVERN-1.7 - Processes for tracking identified AI risks MANAGE-2.4 - Mechanisms for vulnerability remediation and incident response

OWASP LLM Top 10

LLM09:2025 - Misinformation / Overreliance on AI output (via service disruption)

Frequently Asked Questions

What is CVE-2022-41891?

Any TensorFlow-based model serving endpoint is remotely crashable with a single malformed request — no authentication required. If your inference infrastructure runs TF < 2.11/2.10.1/2.9.3/2.8.4 and is network-accessible, patch immediately. This is a trivial availability kill-switch for production ML pipelines.

Is CVE-2022-41891 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2022-41891, increasing the risk of exploitation.

How to fix CVE-2022-41891?

1. Patch: Upgrade to TensorFlow 2.11, 2.10.1, 2.9.3, or 2.8.4 — cherry-pick commit fc33f3dc4c14051a83eec6535b608abe1d355fde if running a custom build. 2. Short-term workaround: Add input validation middleware to inference endpoints that rejects requests with empty element_shape tensors before they reach the TF runtime. 3. Network hardening: Ensure TF Serving is not directly internet-exposed; place behind an API gateway or load balancer that performs basic schema validation. 4. Detection: Monitor for abnormal process restarts or segfault signals in TF Serving logs; alert on SIGSEGV in inference worker processes. 5. Verify: Run `python -c "import tensorflow as tf; print(tf.__version__)"` across all inference nodes to confirm patched versions.

What systems are affected by CVE-2022-41891?

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

What is the CVSS score for CVE-2022-41891?

CVE-2022-41891 has a CVSS v3.1 base score of 7.5 (HIGH). The EPSS exploitation probability is 0.18%.

Technical Details

NVD Description

TensorFlow is an open source platform for machine learning. If `tf.raw_ops.TensorListConcat` is given `element_shape=[]`, it results segmentation fault which can be used to trigger a denial of service attack. We have patched the issue in GitHub commit fc33f3dc4c14051a83eec6535b608abe1d355fde. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

Exploitation Scenario

An adversary identifies a TensorFlow Serving endpoint via passive recon (e.g., exposed gRPC port 8500 or REST port 8501). They craft a PredictRequest invoking a model that uses TensorListConcat — or directly call `tf.raw_ops.TensorListConcat` via a SavedModel with a crafted `element_shape=[]` tensor. The single request triggers a segfault in the TF C++ kernel, crashing the serving process. If the service uses Kubernetes with liveness probes, the pod restarts and the attacker repeats — creating a persistent DoS loop. No credentials, no ML knowledge, no prior access required.