CVE-2022-21738 — MEDIUM (CVSS 6.5) AI Security Vulnerability

CISO Take

A network-accessible integer overflow in TensorFlow's SparseCountSparseOutput kernel allows any authenticated user to crash the TF process — availability impact only, no data exfiltration. Patch immediately to TF 2.8.0, 2.7.1, 2.6.3, or 2.5.3; any TF inference endpoint accepting sparse tensor inputs is directly exposed. Priority is elevated for externally-facing model serving APIs where availability SLAs matter.

Risk Assessment

Medium-risk but operationally significant for AI deployments. CVSS 6.5 with AV:N/AC:L/PR:L reflects easy network exploitation requiring only low-privilege access — no deep ML knowledge needed. Impact is pure availability (crash/restart cycle), not confidentiality or integrity. Risk increases significantly for production inference APIs serving external users, where repeated crashes translate directly to service downtime and SLA violations. No evidence of active exploitation or CISA KEV listing reduces urgency, but the low exploitation barrier warrants prompt patching.

Affected Systems

Package	Ecosystem	Vulnerable Range	Patched
tensorflow	pip	—	No patch
195.0K OpenSSF 7.2 3.7K dependents Pushed 6d ago 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, 2.7.1, 2.6.3, or 2.5.3 (all contain the fix from commit 6f4d3e8).
WORKAROUND

Add server-side validation to reject sparse tensors with suspiciously large dimension values before they reach TF kernels.
ISOLATE

Run TF inference in separate processes with automatic restart (e.g., Kubernetes pod restarts, systemd restart policies) to limit downtime impact.
MONITOR

Alert on abnormal TF process crash rates — repeated crashes from the same source IP indicate exploitation attempts.
NETWORK CONTROL

Restrict access to TF serving endpoints to authenticated, authorized clients only — this vulnerability requires PR:L (low privileges), so unauthenticated exposure significantly expands the attack surface.

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.T0029 - Denial of AI Service AML.T0034 - Cost Harvesting AML.T0049 - Exploit Public-Facing Application

Compliance Impact

This CVE is relevant to:

EU AI Act

Article 15 - Accuracy, robustness and cybersecurity of high-risk AI systems

ISO 42001

8.4 - AI system risk assessment

NIST AI RMF

MANAGE-2.2 - Respond to and recover from AI risks

OWASP LLM Top 10

LLM04 - Model Denial of Service

Frequently Asked Questions

What is CVE-2022-21738?

A network-accessible integer overflow in TensorFlow's SparseCountSparseOutput kernel allows any authenticated user to crash the TF process — availability impact only, no data exfiltration. Patch immediately to TF 2.8.0, 2.7.1, 2.6.3, or 2.5.3; any TF inference endpoint accepting sparse tensor inputs is directly exposed. Priority is elevated for externally-facing model serving APIs where availability SLAs matter.

Is CVE-2022-21738 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2022-21738, increasing the risk of exploitation.

How to fix CVE-2022-21738?

1. PATCH: Upgrade TensorFlow to 2.8.0, 2.7.1, 2.6.3, or 2.5.3 (all contain the fix from commit 6f4d3e8). 2. WORKAROUND: Add server-side validation to reject sparse tensors with suspiciously large dimension values before they reach TF kernels. 3. ISOLATE: Run TF inference in separate processes with automatic restart (e.g., Kubernetes pod restarts, systemd restart policies) to limit downtime impact. 4. MONITOR: Alert on abnormal TF process crash rates — repeated crashes from the same source IP indicate exploitation attempts. 5. NETWORK CONTROL: Restrict access to TF serving endpoints to authenticated, authorized clients only — this vulnerability requires PR:L (low privileges), so unauthenticated exposure significantly expands the attack surface.

What systems are affected by CVE-2022-21738?

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

What is the CVSS score for CVE-2022-21738?

CVE-2022-21738 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 `SparseCountSparseOutput` can be made to crash a TensorFlow process by an integer overflow whose result is then used in a memory allocation. 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 to a TensorFlow model serving API (e.g., a trial account or compromised service credential) crafts a sparse tensor input where dimension values are set near INT_MAX boundaries. When the SparseCountSparseOutput kernel processes this input, integer arithmetic overflows during memory allocation size calculation, triggering an out-of-bounds allocation and process crash. By automating these requests, the adversary achieves sustained DoS against the inference endpoint — effectively taking down fraud detection, recommendation, or NLP services backed by TensorFlow. In multi-tenant ML platforms, a single malicious tenant can crash shared TF workers affecting other tenants.