CVE-2022-23568 — MEDIUM (CVSS 6.5) AI Security Vulnerability

CISO Take

Any TensorFlow deployment on versions 2.5.x–2.7.x that processes sparse tensor inputs from authenticated users is vulnerable to remote crash via integer overflow in AddManySparseToTensorsMap. Patch immediately to TF 2.8.0, 2.7.1, 2.6.3, or 2.5.3. No effective workaround exists beyond restricting access to affected inference endpoints.

Risk Assessment

Medium risk in practice despite easy exploitability. CVSS 6.5: network-reachable, low complexity, only requires low-privilege credentials—any authenticated user can trigger the crash. Impact is strictly availability (no data exfiltration or code execution). Risk escalates for public-facing model serving APIs where user-submitted sparse tensors are processed without upstream input sanitization, and for deployments lacking process auto-restart.

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

0.3%

chance of exploitation in 30 days

Higher than 54% of all CVEs

Source: EPSS v3 — FIRST.org

Exploitation Status

Exploit Available

Exploitation: MEDIUM

Sophistication

Moderate

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

Upgrade to TF 2.8.0, 2.7.1, 2.6.3, or 2.5.3 (patches: commits a68f680 and b51b82f on the TF repo).
If immediate patching is blocked, add input validation middleware to reject SparseTensor inputs with dimension values exceeding expected bounds before they reach TF kernels.
Configure process supervisors (systemd RestartOnFailure, k8s liveness probes) to auto-restart TF serving processes on crash.
Monitor for unexpected TF process exits and spike in 5xx errors from inference endpoints as detection signals.
Audit all API endpoints that accept sparse tensor inputs from external or low-trust callers and apply authentication + rate limiting.

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

ISO 42001

A.9.3 - AI system availability and resilience

NIST AI RMF

MANAGE-2.2 - AI risk treatments including response and recovery

OWASP LLM Top 10

LLM10:2025 - Unbounded Consumption

Frequently Asked Questions

What is CVE-2022-23568?

Any TensorFlow deployment on versions 2.5.x–2.7.x that processes sparse tensor inputs from authenticated users is vulnerable to remote crash via integer overflow in AddManySparseToTensorsMap. Patch immediately to TF 2.8.0, 2.7.1, 2.6.3, or 2.5.3. No effective workaround exists beyond restricting access to affected inference endpoints.

Is CVE-2022-23568 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2022-23568, increasing the risk of exploitation.

How to fix CVE-2022-23568?

1. Upgrade to TF 2.8.0, 2.7.1, 2.6.3, or 2.5.3 (patches: commits a68f680 and b51b82f on the TF repo). 2. If immediate patching is blocked, add input validation middleware to reject SparseTensor inputs with dimension values exceeding expected bounds before they reach TF kernels. 3. Configure process supervisors (systemd RestartOnFailure, k8s liveness probes) to auto-restart TF serving processes on crash. 4. Monitor for unexpected TF process exits and spike in 5xx errors from inference endpoints as detection signals. 5. Audit all API endpoints that accept sparse tensor inputs from external or low-trust callers and apply authentication + rate limiting.

What systems are affected by CVE-2022-23568?

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

What is the CVSS score for CVE-2022-23568?

CVE-2022-23568 has a CVSS v3.1 base score of 6.5 (MEDIUM). The EPSS exploitation probability is 0.30%.

Technical Details

NVD Description

Tensorflow is an Open Source Machine Learning Framework. The implementation of `AddManySparseToTensorsMap` is vulnerable to an integer overflow which results in a `CHECK`-fail when building new `TensorShape` objects (so, an assert failure based denial of service). We are missing some validation on the shapes of the input tensors as well as directly constructing a large `TensorShape` with user-provided dimensions. 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 attacker with a valid low-privilege API credential to a TF Serving endpoint submits a crafted inference request containing a SparseTensor with artificially inflated shape dimensions. When AddManySparseToTensorsMap processes the input, the integer overflow during TensorShape construction triggers a CHECK-fail assertion, immediately crashing the TF serving process. In a Kubernetes deployment without liveness probes, this takes the inference pod offline. By automating a stream of such requests from a single authenticated session, the attacker achieves persistent DoS against the model serving infrastructure with minimal cost and zero specialized ML expertise beyond knowing the target framework.