CVE-2025-62608 — CRITICAL (CVSS 9.1) AI Security Vulnerability

CISO Take

MLX, Apple's ML framework for Apple Silicon, has a critical heap buffer overflow triggered by malicious NumPy .npy files — no authentication or user interaction required. Any team using MLX to load externally-sourced datasets or model weights on Apple hardware is exposed to heap memory disclosure and potential RCE. Patch to 0.29.4 immediately and audit pipelines that ingest .npy files from untrusted sources.

Risk Assessment

CVSS 9.1 Critical with network-reachable, zero-privilege, zero-interaction attack vector makes this technically severe. Practical risk is moderated by low EPSS (0.00095) indicating limited current exploitation activity, and the specific hardware constraint (Apple Silicon). However, MLX adoption is growing rapidly among ML engineers and on-device AI developers — orgs with Apple Silicon MacBooks in ML pipelines have meaningful exposure, especially in dev/research environments where data provenance controls are weak.

Affected Systems

Package	Ecosystem	Vulnerable Range	Patched
mlx	pip	—	No patch
26.1K 283 dependents Pushed yesterday 50% patched ~11d to patch Full package profile →
mlx	pip	<= 0.29.3	`0.29.4`
26.1K 283 dependents Pushed yesterday 50% patched ~11d to patch Full package profile →

Severity & Risk

CVSS 3.1

9.1 / 10

EPSS

0.1%

chance of exploitation in 30 days

Higher than 25% of all CVEs

Source: EPSS v3 — FIRST.org

Exploitation Status

Exploit Available

Exploitation: MEDIUM

Sophistication

Moderate

Exploitation Confidence

medium

○ CISA SSVC: Public PoC

○ 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 High

I None

A High

Recommended Action

6 steps

PATCH

Upgrade mlx to >= 0.29.4 via pip immediately — this is a one-line fix with no breaking changes.
INVENTORY

Identify all systems running MLX (pip list | grep mlx on Apple Silicon endpoints and CI runners).
INPUT VALIDATION

Implement file origin controls — reject .npy files not sourced from trusted, internal artifact registries; add hash verification for dataset files.
ISOLATION

Run MLX data loading in sandboxed processes where possible; restrict access to sensitive memory (credentials, keys) in processes that handle external .npy files.
DETECTION

Monitor for abnormal crashes in mlx processes, unexpected heap access violations in ML workloads, and unusual .npy file sources in pipeline logs.
CI/CD: Add MLX version pinning checks to CI pipelines to prevent regression to vulnerable versions.

CISA SSVC Assessment

Decision Track*

Exploitation poc

Automatable Yes

Technical Impact partial

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

Classification

Supply Chain Code Execution Data Extraction Framework Training Data AML.T0002.000 - Datasets AML.T0010.001 - AI Software AML.T0010.002 - Data AML.T0011.000 - Unsafe AI Artifacts AML.T0025 - Exfiltration via Cyber Means AML.T0049 - Exploit Public-Facing Application

Compliance Impact

This CVE is relevant to:

EU AI Act

Art. 15 - Accuracy, robustness and cybersecurity for high-risk AI systems Art. 9 - Risk management system Article 15 - Accuracy, robustness and cybersecurity

ISO 42001

A.6.1.2 - Information security in AI system development A.6.2 - AI system life cycle — supplier relationships A.8.4 - AI supply chain management

NIST AI RMF

GV-6.1 - Policies for AI risk identification and management of third-party dependencies MANAGE 2.2 - Mechanisms to respond to and recover from AI risks MS-2.5 - Practices and personnel for AI risk management

OWASP LLM Top 10

LLM03 - Training Data Poisoning LLM05:2025 - Supply Chain Vulnerabilities LLM06:2025 - Sensitive Information Disclosure

Frequently Asked Questions

What is CVE-2025-62608?

MLX, Apple's ML framework for Apple Silicon, has a critical heap buffer overflow triggered by malicious NumPy .npy files — no authentication or user interaction required. Any team using MLX to load externally-sourced datasets or model weights on Apple hardware is exposed to heap memory disclosure and potential RCE. Patch to 0.29.4 immediately and audit pipelines that ingest .npy files from untrusted sources.

Is CVE-2025-62608 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2025-62608, increasing the risk of exploitation.

How to fix CVE-2025-62608?

1. PATCH: Upgrade mlx to >= 0.29.4 via pip immediately — this is a one-line fix with no breaking changes. 2. INVENTORY: Identify all systems running MLX (pip list | grep mlx on Apple Silicon endpoints and CI runners). 3. INPUT VALIDATION: Implement file origin controls — reject .npy files not sourced from trusted, internal artifact registries; add hash verification for dataset files. 4. ISOLATION: Run MLX data loading in sandboxed processes where possible; restrict access to sensitive memory (credentials, keys) in processes that handle external .npy files. 5. DETECTION: Monitor for abnormal crashes in mlx processes, unexpected heap access violations in ML workloads, and unusual .npy file sources in pipeline logs. 6. CI/CD: Add MLX version pinning checks to CI pipelines to prevent regression to vulnerable versions.

What systems are affected by CVE-2025-62608?

This vulnerability affects the following AI/ML architecture patterns: training pipelines, data preprocessing pipelines, model fine-tuning workflows, on-device inference, MLOps CI/CD pipelines.

What is the CVSS score for CVE-2025-62608?

CVE-2025-62608 has a CVSS v3.1 base score of 9.1 (CRITICAL). The EPSS exploitation probability is 0.09%.

Technical Details

NVD Description

MLX is an array framework for machine learning on Apple silicon. Prior to version 0.29.4, there is a heap buffer overflow in mlx::core::load() when parsing malicious NumPy .npy files. Attacker-controlled file causes 13-byte out-of-bounds read, leading to crash or information disclosure. This issue has been patched in version 0.29.4.

Exploitation Scenario

Adversary targets an ML engineering team's data ingestion pipeline. They publish a poisoned NumPy dataset to a public repository (Hugging Face, Kaggle, or GitHub) or compromise an internal data storage bucket. A data scientist or automated training pipeline calls mlx.load() on the malicious .npy file on their Apple Silicon MacBook or M-series Mac mini training node. The crafted file triggers the heap buffer overflow in mlx::core::load(), causing a 13-byte out-of-bounds read that leaks adjacent heap memory — potentially exposing API keys, model weights, or other secrets loaded earlier in the process. In a CI/CD context with automated training jobs, this could be fully unattended exploitation.