CVE-2022-29204: TensorFlow DoS — MEDIUM

CISO Take

A missing input validation in TensorFlow's UnsortedSegmentJoin op allows any local low-privilege user to crash ML workloads by passing a negative num_segments value, triggering an assertion failure. Patch to TensorFlow 2.9.0, 2.8.1, 2.7.2, or 2.6.4 immediately—especially on shared ML infrastructure. No data exfiltration or code execution is possible; impact is limited to availability.

What is the risk?

Low-to-medium operational risk. Remote exploitation is impossible (local access required, low privileges). However, risk escalates significantly in shared ML environments—multi-tenant Jupyter hubs, shared training clusters, or internal model-serving APIs—where a malicious or compromised insider can weaponize this trivially. The assertion-based crash leaves no persistence, but can disrupt long-running training jobs or production inference services.

What systems are affected?

Package	Ecosystem	Vulnerable Range	Patched
TensorFlow	pip	—	No patch
195.8K OpenSSF 7.1 3.7K dependents Pushed 3d 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 26% 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?

1 step

1) Patch: Upgrade TensorFlow to ≥2.9.0, ≥2.8.1, ≥2.7.2, or ≥2.6.4. 2) Workaround: Add application-layer validation enforcing num_segments > 0 before calling tf.raw_ops.UnsortedSegmentJoin. 3) Detection: Alert on unexpected TensorFlow process exits and grep TF logs for 'CHECK failed' strings. 4) Harden access: Restrict local execution rights on ML training and serving hosts to trusted users only. 5) Inventory: Audit all TensorFlow versions across dev, staging, and production environments.

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 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

Art. 15 - Accuracy, Robustness and Cybersecurity

ISO 42001

A.6.2.3 - AI System Robustness and Resilience

NIST AI RMF

MANAGE 2.2 - Risk Treatment and Patch Management

Frequently Asked Questions

What is CVE-2022-29204?

A missing input validation in TensorFlow's UnsortedSegmentJoin op allows any local low-privilege user to crash ML workloads by passing a negative num_segments value, triggering an assertion failure. Patch to TensorFlow 2.9.0, 2.8.1, 2.7.2, or 2.6.4 immediately—especially on shared ML infrastructure. No data exfiltration or code execution is possible; impact is limited to availability.

Is CVE-2022-29204 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2022-29204, increasing the risk of exploitation.

How to fix CVE-2022-29204?

1) Patch: Upgrade TensorFlow to ≥2.9.0, ≥2.8.1, ≥2.7.2, or ≥2.6.4. 2) Workaround: Add application-layer validation enforcing num_segments > 0 before calling tf.raw_ops.UnsortedSegmentJoin. 3) Detection: Alert on unexpected TensorFlow process exits and grep TF logs for 'CHECK failed' strings. 4) Harden access: Restrict local execution rights on ML training and serving hosts to trusted users only. 5) Inventory: Audit all TensorFlow versions across dev, staging, and production environments.

What systems are affected by CVE-2022-29204?

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

What is the CVSS score for CVE-2022-29204?

CVE-2022-29204 has a CVSS v3.1 base score of 5.5 (MEDIUM). The EPSS exploitation probability is 0.35%.

What is the AI security impact?

Affected AI Architectures

training pipelinesmodel servinginference endpoints

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: Art. 15

ISO 42001: A.6.2.3

NIST AI RMF: MANAGE 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.UnsortedSegmentJoin` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code assumes `num_segments` is a positive scalar but there is no validation. Since this value is used to allocate the output tensor, a negative value would result in a `CHECK`-failure (assertion failure), as per TFSA-2021-198. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

Exploitation Scenario

An adversary with a low-privilege account on a shared ML training cluster constructs a minimal TensorFlow graph calling tf.raw_ops.UnsortedSegmentJoin with num_segments=-1. On execution, TensorFlow's internal CHECK macro fires and terminates the process. In a Kubernetes-based model-serving deployment, this crashes the inference pod, causing service unavailability until the pod restarts. In a multi-tenant Jupyter environment, a malicious user could repeatedly trigger the crash to disrupt co-tenants' training runs without leaving obvious forensic traces beyond a process crash.

Weaknesses (CWE)

CWE-20 Improper Input Validation Primary CWE-191 Integer Underflow (Wrap or Wraparound) 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.