CVE-2022-29193: TensorFlow DoS — MEDIUM

Q: Is CVE-2022-29193 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2022-29193, increasing the risk of exploitation.

Q: How to fix CVE-2022-29193?

1. PATCH: Upgrade TensorFlow to 2.9.0, 2.8.1, 2.7.2, or 2.6.4. Verify with `pip show tensorflow`. 2. WORKAROUND: Audit code for direct calls to tf.raw_ops.TensorSummaryV2 and validate tensor_dtype and metadata arguments before invocation. 3. MULTI-TENANT HARDENING: Enforce namespace/pod isolation in Kubeflow/JupyterHub to prevent cross-tenant disruption. Apply resource quotas to limit blast radius. 4. DETECTION: Monitor for unexpected TensorFlow process crashes (exit code 134 / SIGABRT) in training infrastructure logs. Alert on repeated abort signals from the same user session. 5. INVENTORY: Identify all ML training pipelines using TensorFlow < 2.6.4 via SBOM or dependency scanning (pip-audit, Safety).

Q: What systems are affected by CVE-2022-29193?

This vulnerability affects the following AI/ML architecture patterns: training pipelines, experiment tracking systems, model monitoring, multi-tenant ML platforms.

Q: What is the CVSS score for CVE-2022-29193?

CVE-2022-29193 has a CVSS v3.1 base score of 5.5 (MEDIUM). The EPSS exploitation probability is 0.32%.

CISO Take

A low-privilege local user can crash TensorFlow processes by passing invalid arguments to tf.raw_ops.TensorSummaryV2, triggering a CHECK-failure. Patch to TF 2.9.0, 2.8.1, 2.7.2, or 2.6.4 immediately — multi-tenant ML environments (shared Jupyter clusters, CI/CD training pipelines) are the primary risk surface. Impact is availability only; no data exfiltration or code execution.

What is the risk?

Effective risk is LOW-MEDIUM despite CVSS 5.5. The local attack vector significantly limits exposure — an attacker needs an authenticated session on the host running TensorFlow. In dedicated single-user workstations the risk is negligible. In shared ML platforms (JupyterHub, Kubeflow, SageMaker Studio multi-tenant), a malicious or compromised tenant could disrupt co-located training jobs. The vulnerability requires no AI/ML expertise and is trivially exploitable once local access is obtained. Not in CISA KEV; no evidence of in-the-wild exploitation.

What systems are affected?

Package	Ecosystem	Vulnerable Range	Patched
TensorFlow	pip	—	No patch
195.8K OpenSSF 7.1 3.7K dependents Pushed 2d ago 4% patched ~1372d to patch Full package profile →

Do you use TensorFlow? You're affected.

How severe is it?

CVSS 3.1

5.5 / 10

EPSS

0.3%

chance of exploitation in 30 days

Higher than 23% 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, VulnCheck KEV, CISA SSVC, EPSS, Metasploit, Exploit-DB, trickest/cve, Nuclei templates, and inthewild.io exploitation reports.

What is the attack surface?

AV Local

AC Low

PR Low

UI None

S Unchanged

C None

I None

A High

What should I do?

5 steps

PATCH

Upgrade TensorFlow to 2.9.0, 2.8.1, 2.7.2, or 2.6.4. Verify with pip show tensorflow.
WORKAROUND

Audit code for direct calls to tf.raw_ops.TensorSummaryV2 and validate tensor_dtype and metadata arguments before invocation.
MULTI-TENANT HARDENING: Enforce namespace/pod isolation in Kubeflow/JupyterHub to prevent cross-tenant disruption. Apply resource quotas to limit blast radius.
DETECTION

Monitor for unexpected TensorFlow process crashes (exit code 134 / SIGABRT) in training infrastructure logs. Alert on repeated abort signals from the same user session.
INVENTORY

Identify all ML training pipelines using TensorFlow < 2.6.4 via SBOM or dependency scanning (pip-audit, Safety).

What does CISA's SSVC say?

Decision Track*

Exploitation poc

Automatable No

Technical Impact partial

Source: CISA Vulnrichment (SSVC v2.0). Decision based on the CISA Coordinator decision tree.

How is it classified?

DoS Framework Training Data AML.T0010.001 - AI Software AML.T0029 - Denial of AI Service AML.T0049 - Exploit Public-Facing Application

Which compliance frameworks are affected?

This CVE is relevant to:

EU AI Act

Article 15 - Accuracy, robustness and cybersecurity

ISO 42001

A.6.2.6 - AI system availability and resilience

NIST AI RMF

MANAGE 2.2 - Mechanisms to sustain and maintain AI risk management MAP 2.2 - Scientific findings and AI risks are monitored

Frequently Asked Questions

What is CVE-2022-29193?

A low-privilege local user can crash TensorFlow processes by passing invalid arguments to tf.raw_ops.TensorSummaryV2, triggering a CHECK-failure. Patch to TF 2.9.0, 2.8.1, 2.7.2, or 2.6.4 immediately — multi-tenant ML environments (shared Jupyter clusters, CI/CD training pipelines) are the primary risk surface. Impact is availability only; no data exfiltration or code execution.

Is CVE-2022-29193 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2022-29193, increasing the risk of exploitation.

How to fix CVE-2022-29193?

1. PATCH: Upgrade TensorFlow to 2.9.0, 2.8.1, 2.7.2, or 2.6.4. Verify with `pip show tensorflow`. 2. WORKAROUND: Audit code for direct calls to tf.raw_ops.TensorSummaryV2 and validate tensor_dtype and metadata arguments before invocation. 3. MULTI-TENANT HARDENING: Enforce namespace/pod isolation in Kubeflow/JupyterHub to prevent cross-tenant disruption. Apply resource quotas to limit blast radius. 4. DETECTION: Monitor for unexpected TensorFlow process crashes (exit code 134 / SIGABRT) in training infrastructure logs. Alert on repeated abort signals from the same user session. 5. INVENTORY: Identify all ML training pipelines using TensorFlow < 2.6.4 via SBOM or dependency scanning (pip-audit, Safety).

What systems are affected by CVE-2022-29193?

This vulnerability affects the following AI/ML architecture patterns: training pipelines, experiment tracking systems, model monitoring, multi-tenant ML platforms.

What is the CVSS score for CVE-2022-29193?

CVE-2022-29193 has a CVSS v3.1 base score of 5.5 (MEDIUM). The EPSS exploitation probability is 0.32%.

What is the AI security impact?

Affected AI Architectures

training pipelinesexperiment tracking systemsmodel monitoringmulti-tenant ML platforms

MITRE ATLAS Techniques

AML.T0010.001 AI Software

AML.T0029 Denial of AI Service

AML.T0049 Exploit Public-Facing Application

Compliance Controls Affected

EU AI Act: Article 15

ISO 42001: A.6.2.6

NIST AI RMF: MANAGE 2.2, MAP 2.2

What are the technical details?

Original Advisory

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.TensorSummaryV2` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

Exploitation Scenario

An insider threat or compromised data-scientist account with shell access to a shared ML training cluster imports TensorFlow and calls tf.raw_ops.TensorSummaryV2 with a malformed metadata argument (e.g., an empty or mismatched dtype). TensorFlow's CHECK macro fires, raising SIGABRT and killing the process. On a Kubernetes-based ML platform without proper pod isolation, this can be repeated in a loop to continuously abort legitimate training jobs belonging to other tenants, effectively conducting a targeted denial-of-service against specific model development efforts — without any elevated privileges.

Weaknesses (CWE)

CWE-20 Improper Input Validation

CWE-20 — Improper Input Validation: The product receives input or data, but it does not validate or incorrectly validates that the input has the properties that are required to process the data safely and correctly.

[Architecture and Design] Consider using language-theoretic security (LangSec) techniques that characterize inputs using a formal language and build "recognizers" for that language. This effectively requires parsing to be a distinct layer that effectively enforces a boundary between raw input and internal data representations, instead of allowing parser code to be scattered throughout the program, where it could be subject to errors or inconsistencies that create weaknesses. [REF-1109] [REF-1110] [REF-1111]
[Architecture and Design] Use an input validation framework such as Struts or the OWASP ESAPI Validation API. Note that using a framework does not automatically address all input validation problems; be mindful of weaknesses that could arise from misusing the framework itself (CWE-1173).

Source: MITRE CWE corpus.