CVE-2020-15200 — MEDIUM (CVSS 5.9) AI Security Vulnerability

Q: Is CVE-2020-15200 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2020-15200, increasing the risk of exploitation.

Q: How to fix CVE-2020-15200?

1. **Patch**: Upgrade TensorFlow to 2.3.1 or later (commit 3cbb917b). 2. **Input validation**: Enforce that `splits[0] == 0` and splits generate a valid partition of the values tensor before processing. 3. **Network segmentation**: Ensure TF Serving endpoints are not directly internet-exposed; place behind authenticated API gateways. 4. **Detection**: Monitor TF serving logs and container restart rates for unexpected segfaults or OOM signals — repeated crashes on specific inputs may indicate exploitation attempts. 5. **Container hygiene**: Run TF serving containers with restricted capabilities and memory limits to reduce blast radius of any memory corruption.

Q: What systems are affected by CVE-2020-15200?

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

Q: What is the CVSS score for CVE-2020-15200?

CVE-2020-15200 has a CVSS v3.1 base score of 5.9 (MEDIUM). The EPSS exploitation probability is 0.28%.

CISO Take

Medium-severity DoS vulnerability in TensorFlow's ragged tensor ops — if your ML inference APIs accept user-supplied sparse/ragged tensor inputs and run TF < 2.3.1, a crafted payload can crash the serving process. Patch to TF 2.3.1+; no active exploitation observed and high attack complexity limits real-world risk. Prioritize if you expose TF serving endpoints to untrusted clients.

Risk Assessment

Effective risk is moderate-low. CVSS 5.9 (AV:N/AC:H) reflects that exploitation requires deliberate crafting of malformed ragged tensor splits, not opportunistic attacks. Primary confirmed impact is availability (segfault/process crash); heap buffer overflow theoretically enables RCE but no working exploit for code execution has been published. Risk spikes for organizations exposing TensorFlow serving endpoints directly to untrusted networks or accepting external tensor payloads without sanitization.

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

5.9 / 10

EPSS

0.3%

chance of exploitation in 30 days

Higher than 51% of all CVEs

Source: EPSS v3 — FIRST.org

Exploitation Status

Exploit Available

Exploitation: MEDIUM

Sophistication

Moderate

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 High

PR None

UI None

S Unchanged

C None

I None

A High

Recommended Action

5 steps

Patch

Upgrade TensorFlow to 2.3.1 or later (commit 3cbb917b).
Input validation

Enforce that splits[0] == 0 and splits generate a valid partition of the values tensor before processing.
Network segmentation

Ensure TF Serving endpoints are not directly internet-exposed; place behind authenticated API gateways.
Detection

Monitor TF serving logs and container restart rates for unexpected segfaults or OOM signals — repeated crashes on specific inputs may indicate exploitation attempts.
Container hygiene

Run TF serving containers with restricted capabilities and memory limits to reduce blast radius of any memory corruption.

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

Art.15 - Accuracy, robustness and cybersecurity Art.9 - Risk management system

ISO 42001

8.4 - AI system risk management

NIST AI RMF

MANAGE-2.2 - Mechanisms for responding to identified AI risks

Frequently Asked Questions

What is CVE-2020-15200?

Medium-severity DoS vulnerability in TensorFlow's ragged tensor ops — if your ML inference APIs accept user-supplied sparse/ragged tensor inputs and run TF < 2.3.1, a crafted payload can crash the serving process. Patch to TF 2.3.1+; no active exploitation observed and high attack complexity limits real-world risk. Prioritize if you expose TF serving endpoints to untrusted clients.

Is CVE-2020-15200 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2020-15200, increasing the risk of exploitation.

How to fix CVE-2020-15200?

1. **Patch**: Upgrade TensorFlow to 2.3.1 or later (commit 3cbb917b). 2. **Input validation**: Enforce that `splits[0] == 0` and splits generate a valid partition of the values tensor before processing. 3. **Network segmentation**: Ensure TF Serving endpoints are not directly internet-exposed; place behind authenticated API gateways. 4. **Detection**: Monitor TF serving logs and container restart rates for unexpected segfaults or OOM signals — repeated crashes on specific inputs may indicate exploitation attempts. 5. **Container hygiene**: Run TF serving containers with restricted capabilities and memory limits to reduce blast radius of any memory corruption.

What systems are affected by CVE-2020-15200?

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

What is the CVSS score for CVE-2020-15200?

CVE-2020-15200 has a CVSS v3.1 base score of 5.9 (MEDIUM). The EPSS exploitation probability is 0.28%.

Technical Details

NVD Description

In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the `splits` tensor generate a valid partitioning of the `values` tensor. Thus, the code sets up conditions to cause a heap buffer overflow. A `BatchedMap` is equivalent to a vector where each element is a hashmap. However, if the first element of `splits_values` is not 0, `batch_idx` will never be 1, hence there will be no hashmap at index 0 in `per_batch_counts`. Trying to access that in the user code results in a segmentation fault. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.

Exploitation Scenario

Attacker targets a recommendation or NLP inference API backed by TensorFlow Serving. They submit a crafted `RaggedTensor` payload via the gRPC API where `splits_values[0] != 0`, causing `batch_idx` to never reach 1. The code attempts to access index 0 of `per_batch_counts` which is empty, triggering a segmentation fault. The TF Serving process crashes and the container restarts. By automating repeated requests with crafted payloads, the attacker keeps the inference pod in a crash loop, effectively taking the ML inference layer offline and degrading dependent services.