CVE-2021-41219 — HIGH (CVSS 7.8) AI Security Vulnerability

CISO Take

TensorFlow versions prior to 2.7.0 contain a heap out-of-bounds access in sparse matrix multiplication triggered by crafting tensors with zero-dimension inputs. While local access is required, this is directly exploitable in shared training environments, Jupyter platforms, or multi-tenant ML infrastructure where users can submit custom operations. Upgrade to TensorFlow 2.7.0, 2.6.1, 2.5.2, or 2.4.4 immediately and audit shared ML environments for user-controlled sparse tensor inputs.

Risk Assessment

Risk is HIGH for shared training infrastructure or Jupyter-based ML platforms where multiple users can execute TensorFlow code. For isolated single-user environments, practical risk is MEDIUM. Low attack complexity combined with low privilege requirements means exploitation is straightforward once local or platform-level access is achieved. No active exploitation evidence in the wild; not in CISA KEV. Organizations running shared GPU clusters or MLaaS offerings face the greatest exposure.

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 →

Do you use tensorflow? You're affected.

Severity & Risk

CVSS 3.1

7.8 / 10

EPSS

0.0%

chance of exploitation in 30 days

Higher than 5% 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 Local

AC Low

PR Low

UI None

S Unchanged

C High

I High

A High

Recommended Action

5 steps

Upgrade TensorFlow to 2.7.0 (primary fix) or apply cherrypick patches to 2.6.1, 2.5.2, or 2.4.4 if a full upgrade is not immediately feasible.
Apply commit e6cf28c72ba2eb949ca950d834dd6d66bb01cfae directly if version pinning constraints prevent upgrading.
Validate tensor dimensions at pipeline ingestion boundaries before passing to sparse ops — reject any tensor with zero or negative dimensions.
In multi-tenant ML platforms, isolate TensorFlow workloads per user or team until patching is complete.
Detection: monitor TF worker processes for crashes or abnormal termination signals when processing sparse operations with atypical tensor shapes.

Classification

Code Execution DoS Framework Training Data AML.T0010.001 - AI Software 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

A.6.2.5 - AI system security

NIST AI RMF

MANAGE 2.2 - Mechanisms to sustain AI risk management are operational

OWASP LLM Top 10

LLM06 - Sensitive Information Disclosure

Frequently Asked Questions

What is CVE-2021-41219?

TensorFlow versions prior to 2.7.0 contain a heap out-of-bounds access in sparse matrix multiplication triggered by crafting tensors with zero-dimension inputs. While local access is required, this is directly exploitable in shared training environments, Jupyter platforms, or multi-tenant ML infrastructure where users can submit custom operations. Upgrade to TensorFlow 2.7.0, 2.6.1, 2.5.2, or 2.4.4 immediately and audit shared ML environments for user-controlled sparse tensor inputs.

Is CVE-2021-41219 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2021-41219, increasing the risk of exploitation.

How to fix CVE-2021-41219?

1. Upgrade TensorFlow to 2.7.0 (primary fix) or apply cherrypick patches to 2.6.1, 2.5.2, or 2.4.4 if a full upgrade is not immediately feasible. 2. Apply commit e6cf28c72ba2eb949ca950d834dd6d66bb01cfae directly if version pinning constraints prevent upgrading. 3. Validate tensor dimensions at pipeline ingestion boundaries before passing to sparse ops — reject any tensor with zero or negative dimensions. 4. In multi-tenant ML platforms, isolate TensorFlow workloads per user or team until patching is complete. 5. Detection: monitor TF worker processes for crashes or abnormal termination signals when processing sparse operations with atypical tensor shapes.

What systems are affected by CVE-2021-41219?

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

What is the CVSS score for CVE-2021-41219?

CVE-2021-41219 has a CVSS v3.1 base score of 7.8 (HIGH). The EPSS exploitation probability is 0.02%.

Technical Details

NVD Description

TensorFlow is an open source platform for machine learning. In affected versions the code for sparse matrix multiplication is vulnerable to undefined behavior via binding a reference to `nullptr`. This occurs whenever the dimensions of `a` or `b` are 0 or less. In the case on one of these is 0, an empty output tensor should be allocated (to conserve the invariant that output tensors are always allocated when the operation is successful) but nothing should be written to it (that is, we should return early from the kernel implementation). Otherwise, attempts to write to this empty tensor would result in heap OOB access. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

Exploitation Scenario

An adversary with access to a shared ML training platform — such as a data scientist on a multi-user Jupyter environment or a malicious insider on a GPU cluster — submits a training job invoking sparse matrix multiplication with a tensor where one dimension is set to 0. TensorFlow attempts to bind a reference to nullptr and then writes to the resulting empty output tensor, triggering heap OOB access. In a containerized training environment, this could crash the worker process (DoS to ongoing training runs) or, with additional heap grooming, achieve code execution within the TF process context, potentially exfiltrating model weights or training data resident in memory.