CVE-2021-29617 — MEDIUM (CVSS 5.5) AI Security Vulnerability

Q: Is CVE-2021-29617 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2021-29617, increasing the risk of exploitation.

Q: How to fix CVE-2021-29617?

1. Upgrade to TensorFlow ≥2.1.4, ≥2.2.3, ≥2.3.3, ≥2.4.2, or ≥2.5.0 depending on the branch in use. 2. If immediate patching is blocked, validate and sanitize arguments passed to tf.strings.substr before invocation — specifically enforce valid position and length bounds. 3. Restrict local system access to TensorFlow processes via OS-level controls and least-privilege principles. 4. Monitor for abnormal TensorFlow process crashes as a potential indicator of exploitation attempts in shared environments.

Q: What systems are affected by CVE-2021-29617?

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

Q: What is the CVSS score for CVE-2021-29617?

CVE-2021-29617 has a CVSS v3.1 base score of 5.5 (MEDIUM). The EPSS exploitation probability is 0.02%.

CISO Take

A local attacker with low-privilege access can crash TensorFlow processes by passing invalid arguments to tf.strings.substr, triggering a CHECK-fail denial of service. The local-only attack vector significantly limits blast radius — remote exploitation requires a separate vulnerability to achieve local access first. Patch to TensorFlow 2.1.4+, 2.2.3+, 2.3.3+, 2.4.2+, or 2.5.0+ to remediate.

Risk Assessment

Low-to-medium operational risk. CVSS 5.5 with a local attack vector and low privileges keeps real-world exploitation unlikely (EPSS: 0.0005, not in CISA KEV). Impact is availability-only — no data exfiltration or integrity compromise. Risk elevates in multi-tenant ML environments (shared GPU clusters, hosted Jupyter notebooks) where local access is more easily achievable by untrusted users.

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 →
tensorflow	pip	< 2.1.4	`2.1.4`
195.0K OpenSSF 7.2 3.7K dependents Pushed today 4% patched ~1372d to patch Full package profile →
tensorflow-cpu	pip	< 2.1.4	`2.1.4`
195.0K OpenSSF 7.2 3.7K dependents Pushed today 4% patched ~1372d to patch Full package profile →
tensorflow-gpu	pip	< 2.1.4	`2.1.4`
195.0K OpenSSF 7.2 3.7K dependents Pushed today 4% patched ~1372d to patch Full package profile →

Severity & Risk

CVSS 3.1

5.5 / 10

EPSS

0.0%

chance of exploitation in 30 days

Higher than 6% 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 Local

AC Low

PR Low

UI None

S Unchanged

C None

I None

A High

Recommended Action

4 steps

Upgrade to TensorFlow ≥2.1.4, ≥2.2.3, ≥2.3.3, ≥2.4.2, or ≥2.5.0 depending on the branch in use.
If immediate patching is blocked, validate and sanitize arguments passed to tf.strings.substr before invocation — specifically enforce valid position and length bounds.
Restrict local system access to TensorFlow processes via OS-level controls and least-privilege principles.
Monitor for abnormal TensorFlow process crashes as a potential indicator of exploitation attempts in shared environments.

Classification

DoS Framework AML.T0010.001 - AI Software AML.T0029 - Denial of AI Service

Compliance Impact

This CVE is relevant to:

EU AI Act

Art. 15 - Accuracy, Robustness and Cybersecurity

ISO 42001

6.1.2 - AI Risk Assessment — Availability

NIST AI RMF

MANAGE-2.2 - Treatment of Identified AI Risks

Frequently Asked Questions

What is CVE-2021-29617?

A local attacker with low-privilege access can crash TensorFlow processes by passing invalid arguments to tf.strings.substr, triggering a CHECK-fail denial of service. The local-only attack vector significantly limits blast radius — remote exploitation requires a separate vulnerability to achieve local access first. Patch to TensorFlow 2.1.4+, 2.2.3+, 2.3.3+, 2.4.2+, or 2.5.0+ to remediate.

Is CVE-2021-29617 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2021-29617, increasing the risk of exploitation.

How to fix CVE-2021-29617?

1. Upgrade to TensorFlow ≥2.1.4, ≥2.2.3, ≥2.3.3, ≥2.4.2, or ≥2.5.0 depending on the branch in use. 2. If immediate patching is blocked, validate and sanitize arguments passed to tf.strings.substr before invocation — specifically enforce valid position and length bounds. 3. Restrict local system access to TensorFlow processes via OS-level controls and least-privilege principles. 4. Monitor for abnormal TensorFlow process crashes as a potential indicator of exploitation attempts in shared environments.

What systems are affected by CVE-2021-29617?

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

What is the CVSS score for CVE-2021-29617?

CVE-2021-29617 has a CVSS v3.1 base score of 5.5 (MEDIUM). The EPSS exploitation probability is 0.02%.

Technical Details

NVD Description

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service via `CHECK`-fail in `tf.strings.substr` with invalid arguments. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

Exploitation Scenario

An adversary with low-privilege local access to a shared ML training server crafts a Python script calling tf.strings.substr with invalid arguments (e.g., out-of-bounds position or negative length). The internal CHECK assertion fails, immediately crashing the TensorFlow process. In a shared Jupyter notebook environment or multi-tenant ML platform, this disrupts concurrent training jobs and inference services belonging to other users — potentially causing costly training restarts or SLA violations without any permanent damage to data or models.