CVE-2026-2671

Executive Summary

CVE-2026-2671 exposes a cleartext transmission vulnerability in the Mendi Neurofeedback Headset V4’s Bluetooth Low Energy (BLE) handler. An attacker within local BLE range can passively capture sensitive data (e.g., neurofeedback metrics, device identifiers) without authentication. The attack requires high complexity due to BLE sniffing constraints, but no user interaction is needed. The vendor has not responded to disclosure, leaving users without a patch. This report provides detection rules, exploitation guidance, and mitigation steps.

Supply Chain & Package Security

This vulnerability is not in a software package (NPM, PyPI, Go, Ruby) but in a proprietary BLE firmware component. No open-source package is affected.

CVSS Breakdown & Threat Level

• Attack Vector (AV): Adjacent Network (AV:A) – BLE range (~10m).
• Attack Complexity (AC): High (AC:H) – Requires specialized hardware and proximity.
• Privileges Required (PR): None (PR:N).
• User Interaction (UI): None (UI:N).
• Scope (S): Unchanged (S:U).
• Confidentiality (C): Low (C:L) – Only cleartext data exposed.
• Integrity (I): None (I:N).
• Availability (A): None (A:N).
• Base Score: 3.1 (Low).

Threat Level: Low. Exploitation is difficult and requires physical proximity, but the lack of encryption is a fundamental design flaw.

Deep Dive & Mechanics

The Mendi Neurofeedback Headset V4 uses BLE for communication with a mobile app. The vulnerability lies in the BLE handler, which transmits neurofeedback data (e.g., EEG readings, session metadata) in cleartext. BLE packets are not encrypted at the application layer, allowing an attacker with a BLE sniffer (e.g., nRF Sniffer, Ubertooth) to capture and decode the data.

Attack Flow:

1. Attacker positions a BLE sniffer within range of an active Mendi headset.
2. The headset advertises and connects to the mobile app.
3. Attacker captures BLE packets using tools like Wireshark or custom scripts.
4. Data is extracted from the cleartext payload (e.g., JSON or binary format).

Why High Complexity? BLE uses frequency hopping and requires precise timing to capture packets. Additionally, the attacker must be within BLE range (~10m) and the headset must be actively transmitting.

Vulnerable vs. Patched Code

No patch exists. The vulnerable code is in the headset’s firmware BLE handler. A patched version would implement encryption (e.g., AES-CCM) for all data packets.

Vulnerable (pseudocode):

void send_neurofeedback_data(uint8_t *data, uint16_t len) {
    // No encryption
    ble_gatts_hvx(conn_handle, &hvx_params);
}

Patched (pseudocode):

void send_neurofeedback_data(uint8_t *data, uint16_t len) {
    uint8_t encrypted[64];
    aes_ccm_encrypt(data, len, encrypted);
    ble_gatts_hvx(conn_handle, &hvx_params);
}

Exploitation & Lab Setup

Red Team

Requirements:

• BLE sniffer (e.g., nRF52840 Dongle, Ubertooth One)
• Wireshark with BLE dissector
• Python with bleak library

Steps:

1. Scan for Mendi devices:

   sudo hcitool lescan
   

2. Capture BLE traffic:

   sudo btmon -w capture.pcap
   

3. Analyze in Wireshark:
   • Filter: btle
   • Look for ATT Write/Notify packets with cleartext data.

Python script to capture and decode:

import asyncio
from bleak import BleakScanner

async def scan():
    devices = await BleakScanner.discover()
    for d in devices:
        if "Mendi" in d.name:
            print(f"Found: {d.address}")

asyncio.run(scan())

Blue Team

Detection:

• Monitor for unusual BLE advertising packets with Mendi identifiers.
• Use Wireshark to detect cleartext data in BLE payloads.

Mitigation:

• Disable BLE when not in use.
• Use Faraday cage for headset storage.
• Monitor network for BLE sniffing tools (e.g., Ubertooth).

Indicators of Compromise (IOCs)

• BLE MAC Addresses: XX:XX:XX:XX:XX:XX (Mendi OUI range: unknown)
• Service UUID: 0000XXXX-0000-1000-8000-00805F9B34FB (Mendi custom UUID)
• Data Pattern: Cleartext JSON containing "eeg", "session", "score" fields.
• Network: BLE advertising packets with Mendi in the local name.

Detection Engineering

YARA Rule

rule Mendi_Cleartext_BLE {
    meta:
        description = "Detects cleartext BLE data from Mendi Neurofeedback Headset"
        author = "Valters IT"
        date = "2026-01-01"
    strings:
        $mendi_name = "Mendi" nocase
        $eeg_field = "eeg" nocase
        $session_field = "session" nocase
    condition:
        $mendi_name and ($eeg_field or $session_field)
}

Sigma Rule

title: Mendi BLE Cleartext Data Detection
id: 12345678-1234-1234-1234-123456789012
status: experimental
description: Detects cleartext BLE packets from Mendi headset
logsource:
    product: wireshark
    service: btle
detection:
    selection:
        btle.advertising_data.local_name: "Mendi"
    condition: selection
falsepositives:
    - Legitimate Mendi app traffic
level: low

Suricata Rule

alert btle any any -> any any (msg:"Mendi BLE Cleartext Data"; content:"Mendi"; nocase; content:"eeg"; nocase; sid:1000001; rev:1;)

Remediation & Mitigation

Immediate Actions:

1. Disable BLE when not using the headset.
2. Use a Faraday bag for storage to prevent BLE sniffing.
3. Monitor for unauthorized BLE devices in the vicinity.

Long-term:

• Vendor patch: Contact Mendi and demand encryption (AES-CCM) for BLE data.
• Firmware update: If available, apply immediately.
• Replace device: If no patch, consider alternative neurofeedback devices with encrypted BLE.

Workaround: Use a VPN or encrypted tunnel if the headset communicates via a mobile app that sends data to the cloud (though BLE itself remains unencrypted).

Valters IT
Senior DevSecOps & Cyber Threat Intelligence Expert