CVE-2023-27506 — HIGH (CVSS 7.8) AI Security Vulnerability

CISO Take

Intel's optimized TensorFlow distribution (pre-2.12) has a buffer restriction flaw enabling local privilege escalation with low skill required. Any shared ML compute environment—Jupyter hubs, Ray clusters, Slurm HPC nodes—is at risk when multiple users share the same host. Patch to Intel Optimization for TensorFlow 2.12+ immediately and isolate ML workloads into separate containers.

Risk Assessment

CVSS 7.8 High with low attack complexity and no user interaction required makes this straightforward to exploit given local access. In enterprise ML environments, local access is routinely granted to data scientists and ML engineers on shared GPU/CPU clusters running Intel Xeon infrastructure. Not in CISA KEV and no public exploits confirmed, but the C:H/I:H/A:H impact triad on shared multi-tenant ML nodes warrants urgent remediation—one compromised ML engineer account can become root.

Affected Systems

Package	Ecosystem	Vulnerable Range	Patched
optimization_for_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 optimization_for_tensorflow? You're affected.

Severity & Risk

CVSS 3.1

7.8 / 10

EPSS

0.1%

chance of exploitation in 30 days

Higher than 20% of all CVEs

Source: EPSS v3 — FIRST.org

Exploitation Status

No known exploitation

Sophistication

Moderate

Attack Surface

AV Local

AC Low

PR Low

UI None

S Unchanged

C High

I High

A High

Recommended Action

5 steps

Patch: Update Intel Optimization for TensorFlow to 2.12+ immediately—run 'pip list | grep intel-tensorflow' across all ML nodes to identify exposure.
Isolate: Containerize ML workloads (Docker/Kubernetes) so privilege escalation cannot cross tenant boundaries.
Least privilege: Restrict local shell access to ML training nodes; use SSH bastion with MFA.
Detect: Monitor for unexpected setuid/sudo usage and anomalous process ownership changes on ML infrastructure.
Audit: Review Intel SA-00840 advisory for any additional mitigations specific to your hardware configuration.

CISA SSVC Assessment

Decision Track

Exploitation none

Automatable No

Technical Impact partial

Source: CISA Vulnrichment (SSVC v2.0). Decision based on the CISA Coordinator decision tree.

Classification

Code Execution Supply Chain Framework Training Data AML.T0010.001 - AI Software AML.T0037 - Data from Local System AML.T0106 - Exploitation for Credential Access

Compliance Impact

This CVE is relevant to:

EU AI Act

Article 15 - Accuracy, robustness and cybersecurity

ISO 42001

A.8.3 - Information security for AI systems

NIST AI RMF

MANAGE 2.2 - Mechanisms are in place and applied to sustain the value of deployed AI systems and to manage risks over the AI system lifecycle

OWASP LLM Top 10

LLM03:2025 - Supply Chain Vulnerabilities

Frequently Asked Questions

What is CVE-2023-27506?

Intel's optimized TensorFlow distribution (pre-2.12) has a buffer restriction flaw enabling local privilege escalation with low skill required. Any shared ML compute environment—Jupyter hubs, Ray clusters, Slurm HPC nodes—is at risk when multiple users share the same host. Patch to Intel Optimization for TensorFlow 2.12+ immediately and isolate ML workloads into separate containers.

Is CVE-2023-27506 actively exploited?

No confirmed active exploitation of CVE-2023-27506 has been reported, but organizations should still patch proactively.

How to fix CVE-2023-27506?

1. Patch: Update Intel Optimization for TensorFlow to 2.12+ immediately—run 'pip list | grep intel-tensorflow' across all ML nodes to identify exposure. 2. Isolate: Containerize ML workloads (Docker/Kubernetes) so privilege escalation cannot cross tenant boundaries. 3. Least privilege: Restrict local shell access to ML training nodes; use SSH bastion with MFA. 4. Detect: Monitor for unexpected setuid/sudo usage and anomalous process ownership changes on ML infrastructure. 5. Audit: Review Intel SA-00840 advisory for any additional mitigations specific to your hardware configuration.

What systems are affected by CVE-2023-27506?

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

What is the CVSS score for CVE-2023-27506?

CVE-2023-27506 has a CVSS v3.1 base score of 7.8 (HIGH). The EPSS exploitation probability is 0.06%.

Technical Details

NVD Description

Improper buffer restrictions in the Intel(R) Optimization for Tensorflow software before version 2.12 may allow an authenticated user to potentially enable escalation of privilege via local access.

Exploitation Scenario

An authenticated data scientist on a shared Jupyter notebook server crafts a malicious TensorFlow workload that triggers the buffer restriction flaw in Intel's optimized TF runtime. By exploiting CWE-119 (out-of-bounds memory write), they overwrite adjacent memory structures to execute code as root. From root, they exfiltrate competing teams' proprietary model weights, training datasets containing PII, and environment variables holding API keys for cloud services—all without triggering typical security controls since the initial access was fully legitimate.