CVE-2020-15194 — MEDIUM (CVSS 5.3) AI Security Vulnerability

CISO Take

A medium-severity denial-of-service vulnerability in TensorFlow's sparse gradient operation allows unauthenticated remote attackers to crash serving infrastructure by sending malformed tensor inputs. Any public-facing TensorFlow Serving endpoint that processes sparse tensor operations is at risk of availability disruption. Patch immediately to TF 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1 and restrict direct access to inference endpoints where possible.

Risk Assessment

Medium risk with elevated operational impact for production ML serving environments. The network-accessible attack vector with no authentication required makes this trivially exploitable, though impact is limited to availability with no data exfiltration or code execution potential. Risk escalates significantly for organizations running TensorFlow Serving in production without input validation layers or network segmentation — a single crafted request is sufficient to crash the serving process and cause downtime.

Affected Systems

Package	Ecosystem	Vulnerable Range	Patched
tensorflow	pip	—	No patch
195.0K OpenSSF 7.2 3.7K dependents Pushed today 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 44% 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 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1 — fix is commit 390611e0d45c5793c7066110af37c8514e6a6c54.
Network controls: Place an API gateway or reverse proxy in front of TF Serving; reject malformed or unexpected tensor shape payloads before they reach the TF runtime.
Input validation: Implement server-side shape validation for tensor inputs prior to gradient operations.
Detection: Monitor TF Serving process crash/restart rates and assertion failure logs as indicators of exploitation attempts.
Isolation: Run TF Serving in containers with automatic restart policies to reduce downtime impact if exploited before patching.

Classification

DoS Framework Inference AML.T0029 - Denial of AI Service AML.T0043 - Craft Adversarial Data AML.T0049 - Exploit Public-Facing Application

Compliance Impact

This CVE is relevant to:

EU AI Act

Art. 15 - Accuracy, robustness and cybersecurity

ISO 42001

8.4 - AI system operation

NIST AI RMF

GOVERN 1.7 - Processes and procedures for AI risk management MANAGE 2.2 - Mechanisms to sustain and manage AI risk

Frequently Asked Questions

What is CVE-2020-15194?

A medium-severity denial-of-service vulnerability in TensorFlow's sparse gradient operation allows unauthenticated remote attackers to crash serving infrastructure by sending malformed tensor inputs. Any public-facing TensorFlow Serving endpoint that processes sparse tensor operations is at risk of availability disruption. Patch immediately to TF 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1 and restrict direct access to inference endpoints where possible.

Is CVE-2020-15194 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2020-15194, increasing the risk of exploitation.

How to fix CVE-2020-15194?

1. Patch: Upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1 — fix is commit 390611e0d45c5793c7066110af37c8514e6a6c54. 2. Network controls: Place an API gateway or reverse proxy in front of TF Serving; reject malformed or unexpected tensor shape payloads before they reach the TF runtime. 3. Input validation: Implement server-side shape validation for tensor inputs prior to gradient operations. 4. Detection: Monitor TF Serving process crash/restart rates and assertion failure logs as indicators of exploitation attempts. 5. Isolation: Run TF Serving in containers with automatic restart policies to reduce downtime impact if exploited before patching.

What systems are affected by CVE-2020-15194?

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

What is the CVSS score for CVE-2020-15194?

CVE-2020-15194 has a CVSS v3.1 base score of 5.3 (MEDIUM). The EPSS exploitation probability is 0.22%.

Technical Details

NVD Description

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `SparseFillEmptyRowsGrad` implementation has incomplete validation of the shapes of its arguments. Although `reverse_index_map_t` and `grad_values_t` are accessed in a similar pattern, only `reverse_index_map_t` is validated to be of proper shape. Hence, malicious users can pass a bad `grad_values_t` to trigger an assertion failure in `vec`, causing denial of service in serving installations. The issue is patched in commit 390611e0d45c5793c7066110af37c8514e6a6c54, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1."

Exploitation Scenario

An adversary identifies a public-facing TensorFlow Serving API — common in federated learning services or model retraining pipelines. Without authentication, they craft an inference request where grad_values_t carries a shape that passes top-level checks but triggers a shape mismatch assertion inside the vec() call of SparseFillEmptyRowsGrad. The serving process crashes immediately. With no rate limiting in place, the adversary scripts repeated requests to maintain a persistent DoS condition, keeping the ML inference service unavailable and potentially triggering costly auto-scaling loops or SLA breaches.