CVE-2022-23595 — MEDIUM (CVSS 6.5) AI Security Vulnerability

CISO Take

A low-privileged remote user can crash TensorFlow processes by triggering the XLA compilation cache under default device settings, causing a null pointer dereference. Impact is availability-only — no data exfiltration risk — but production ML serving infrastructure can be taken down with minimal effort. Patch immediately to TensorFlow 2.8.0, 2.7.1, 2.6.3, or 2.5.3.

Risk Assessment

Medium risk overall, but elevated for organizations running TensorFlow as a shared ML platform or exposed inference service. CVSS 6.5 reflects network reachability, low attack complexity, and low privilege requirement — a low bar for disruption. Availability impact is high (process crash), but no confidentiality or integrity exposure. Not in CISA KEV and no reported active exploitation as of enrichment date, but the vulnerability is trivially triggered given default TensorFlow settings.

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

4 steps

PATCH

Upgrade to TensorFlow 2.8.0, 2.7.1, 2.6.3, or 2.5.3. Apply commit e21af685e1828f7ca65038307df5cc06de4479e8 if building from source.
WORKAROUND

If patching is delayed, restrict who can submit TensorFlow jobs to the platform. Enforce network-level authentication before XLA-enabled endpoints.
DETECTION

Monitor TensorFlow process crash logs for null pointer dereference signatures in xla_platform_info.cc. Alert on unexpected process restarts in ML serving infra.
INVENTORY

Identify all TensorFlow deployments running versions 2.5.x, 2.6.x, 2.7.x in your environment — prioritize those with network-exposed inference endpoints.

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

Article 9 - Risk management system

ISO 42001

A.6.2.8 - AI system operation and monitoring

NIST AI RMF

MANAGE 2.2 - Mechanisms are in place to sustain the value of deployed AI systems

OWASP LLM Top 10

LLM10 - Model Denial of Service

Frequently Asked Questions

What is CVE-2022-23595?

A low-privileged remote user can crash TensorFlow processes by triggering the XLA compilation cache under default device settings, causing a null pointer dereference. Impact is availability-only — no data exfiltration risk — but production ML serving infrastructure can be taken down with minimal effort. Patch immediately to TensorFlow 2.8.0, 2.7.1, 2.6.3, or 2.5.3.

Is CVE-2022-23595 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2022-23595, increasing the risk of exploitation.

How to fix CVE-2022-23595?

1. PATCH: Upgrade to TensorFlow 2.8.0, 2.7.1, 2.6.3, or 2.5.3. Apply commit e21af685e1828f7ca65038307df5cc06de4479e8 if building from source. 2. WORKAROUND: If patching is delayed, restrict who can submit TensorFlow jobs to the platform. Enforce network-level authentication before XLA-enabled endpoints. 3. DETECTION: Monitor TensorFlow process crash logs for null pointer dereference signatures in xla_platform_info.cc. Alert on unexpected process restarts in ML serving infra. 4. INVENTORY: Identify all TensorFlow deployments running versions 2.5.x, 2.6.x, 2.7.x in your environment — prioritize those with network-exposed inference endpoints.

What systems are affected by CVE-2022-23595?

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

What is the CVSS score for CVE-2022-23595?

CVE-2022-23595 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. When building an XLA compilation cache, if default settings are used, TensorFlow triggers a null pointer dereference. In the default scenario, all devices are allowed, so `flr->config_proto` is `nullptr`. 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-privileged access to a shared ML platform (e.g., a data scientist account, a compromised service account, or an internal user on a multi-tenant training cluster) submits a TensorFlow graph that invokes XLA compilation. Under default settings where all devices are allowed, flr->config_proto is nullptr and the XLA platform info builder dereferences it, crashing the TensorFlow runtime process. On a shared inference server, this takes down model serving for all tenants. The attack requires no special ML knowledge — just triggering any XLA-compiled operation against a vulnerable TensorFlow version.