CVE-2020-15191 — MEDIUM (CVSS 5.3) AI Security Vulnerability

Q: Is CVE-2020-15191 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2020-15191, increasing the risk of exploitation.

Q: How to fix CVE-2020-15191?

1. Patch: Upgrade to TensorFlow 2.2.1 or 2.3.1 (commit 22e07fb). 2. Verify: Run `python -c 'import tensorflow as tf; print(tf.__version__)'` across all serving and training nodes. 3. Interim workaround: Add input validation middleware on TF Serving endpoints to reject malformed dlpack requests; restrict network access to trusted clients only. 4. Detection: Monitor for sudden TF Serving process restarts or OOM crashes correlated with unexpected API traffic patterns — they may indicate exploit attempts. 5. Audit: Check container images and model serving infrastructure for pinned TF versions that may have never been updated.

Q: What systems are affected by CVE-2020-15191?

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

Q: What is the CVSS score for CVE-2020-15191?

CVE-2020-15191 has a CVSS v3.1 base score of 5.3 (MEDIUM). The EPSS exploitation probability is 0.25%.

CISO Take

TensorFlow versions before 2.2.1/2.3.1 allow unauthenticated remote attackers to crash model serving processes via a crafted dlpack argument — no exploit code needed, just a malformed API call. If you're still running TensorFlow < 2.3.1 in any serving or inference environment, patch now. This is a 2020 vulnerability; if it's unpatched in your environment, treat it as a critical operational gap.

Risk Assessment

Despite a medium CVSS score (5.3), the attack profile is concerning: network-accessible, zero privileges required, no user interaction, low complexity. An attacker can reliably crash a TensorFlow inference service from the internet. The blast radius is limited to availability — no code execution or data exposure — but in production ML pipelines, availability directly impacts business continuity. Risk elevates in shared multi-tenant inference environments where one crash affects multiple tenants.

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 →
leap	—	—	No patch

Severity & Risk

CVSS 3.1

5.3 / 10

EPSS

0.2%

chance of exploitation in 30 days

Higher than 48% of all CVEs

Source: EPSS v3 — FIRST.org

Exploitation Status

Exploit Available

Exploitation: MEDIUM

Sophistication

Trivial

Exploitation Confidence

medium

○ 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 None

UI None

S Unchanged

C None

I None

A Low

Recommended Action

5 steps

Patch: Upgrade to TensorFlow 2.2.1 or 2.3.1 (commit 22e07fb).
Verify: Run python -c 'import tensorflow as tf; print(tf.__version__)' across all serving and training nodes.
Interim workaround: Add input validation middleware on TF Serving endpoints to reject malformed dlpack requests; restrict network access to trusted clients only.
Detection: Monitor for sudden TF Serving process restarts or OOM crashes correlated with unexpected API traffic patterns — they may indicate exploit attempts.
Audit: Check container images and model serving infrastructure for pinned TF versions that may have never been updated.

Classification

DoS Code Execution 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.6.2.5 - AI system vulnerability management

NIST AI RMF

GOVERN 1.4 - Organizational roles and responsibilities for AI risk MANAGE 2.2 - Mechanisms to update and patch AI systems

Frequently Asked Questions

What is CVE-2020-15191?

TensorFlow versions before 2.2.1/2.3.1 allow unauthenticated remote attackers to crash model serving processes via a crafted dlpack argument — no exploit code needed, just a malformed API call. If you're still running TensorFlow < 2.3.1 in any serving or inference environment, patch now. This is a 2020 vulnerability; if it's unpatched in your environment, treat it as a critical operational gap.

Is CVE-2020-15191 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2020-15191, increasing the risk of exploitation.

How to fix CVE-2020-15191?

1. Patch: Upgrade to TensorFlow 2.2.1 or 2.3.1 (commit 22e07fb). 2. Verify: Run `python -c 'import tensorflow as tf; print(tf.__version__)'` across all serving and training nodes. 3. Interim workaround: Add input validation middleware on TF Serving endpoints to reject malformed dlpack requests; restrict network access to trusted clients only. 4. Detection: Monitor for sudden TF Serving process restarts or OOM crashes correlated with unexpected API traffic patterns — they may indicate exploit attempts. 5. Audit: Check container images and model serving infrastructure for pinned TF versions that may have never been updated.

What systems are affected by CVE-2020-15191?

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

What is the CVSS score for CVE-2020-15191?

CVE-2020-15191 has a CVSS v3.1 base score of 5.3 (MEDIUM). The EPSS exploitation probability is 0.25%.

Technical Details

NVD Description

In Tensorflow before versions 2.2.1 and 2.3.1, if a user passes an invalid argument to `dlpack.to_dlpack` the expected validations will cause variables to bind to `nullptr` while setting a `status` variable to the error condition. However, this `status` argument is not properly checked. Hence, code following these methods will bind references to null pointers. This is undefined behavior and reported as an error if compiling with `-fsanitize=null`. The issue is patched in commit 22e07fb204386768e5bcbea563641ea11f96ceb8 and is released in TensorFlow versions 2.2.1, or 2.3.1.

Exploitation Scenario

An adversary targeting a competitor's ML inference API discovers TensorFlow is in use (via error messages, headers, or public documentation). They craft an HTTP request to the model serving endpoint with an intentionally malformed dlpack tensor descriptor. The invalid argument bypasses input validation, binds internal variables to null pointers, and causes a null pointer dereference crash. The inference service goes down. In a Kubernetes deployment without proper restart policies, this causes an SLA breach. A sophisticated attacker could loop this to create persistent denial of service against a paid ML API service.