detecting-bluetooth-low-energy-attacks

mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026

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$npx skills install mukul975/Anthropic-Cybersecurity-Skills/detecting-bluetooth-low-energy-attacks
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summary

Detects and analyzes Bluetooth Low Energy (BLE) security attacks including sniffing, replay attacks, GATT enumeration abuse, and Man-in-the-Middle interception. Uses Ubertooth One and nRF52840 sniffers for packet capture, the bleak Python library for GATT service enumeration, and crackle for BLE encryption cracking. Use when assessing IoT device BLE security, monitoring for BLE-based attacks on wireless infrastructure, or performing authorized BLE penetration testing. Activates for requests involving BLE security assessment, Ubertooth sniffing, GATT enumeration, or BLE replay detection.

skill.md
name
detecting-bluetooth-low-energy-attacks
description
'Detects and analyzes Bluetooth Low Energy (BLE) security attacks including sniffing, replay attacks, GATT enumeration abuse, and Man-in-the-Middle interception. Uses Ubertooth One and nRF52840 sniffers for packet capture, the bleak Python library for GATT service enumeration, and crackle for BLE encryption cracking. Use when assessing IoT device BLE security, monitoring for BLE-based attacks on wireless infrastructure, or performing authorized BLE penetration testing. Activates for requests involving BLE security assessment, Ubertooth sniffing, GATT enumeration, or BLE replay detection. '
domain
cybersecurity
subdomain
wireless-security
author
mukul975
tags
- ble - bluetooth - ubertooth - nrf-sniffer - gatt - wireless-security - iot-security - replay-attack
version
1.0.0
license
Apache-2.0
nist_csf
- PR.IR-01 - DE.CM-01 - ID.AM-03

Detecting Bluetooth Low Energy Attacks

Disclaimer

This skill is intended for authorized security testing, penetration testing engagements, CTF competitions, and educational purposes only. Sniffing, intercepting, or manipulating Bluetooth communications without authorization may violate federal wiretapping laws and local regulations. Always obtain explicit written permission before conducting any wireless security assessment.

When to Use

Use this skill when:

  • Performing authorized BLE security assessments of IoT devices, medical devices, or smart locks
  • Monitoring a wireless environment for BLE-based replay attacks, spoofing, or unauthorized enumeration
  • Analyzing BLE packet captures to detect Man-in-the-Middle attacks or pairing exploitation
  • Enumerating GATT services and characteristics to identify insecure read/write permissions on BLE peripherals
  • Assessing BLE encryption strength and testing for crackable pairing exchanges
  • Building BLE intrusion detection capabilities for wireless security monitoring

Do not use for intercepting BLE communications without explicit authorization. Do not deploy BLE scanning tools in environments where wireless monitoring is prohibited.

Prerequisites

  • Ubertooth One hardware for passive BLE sniffing, or Nordic nRF52840 USB Dongle with nRF Sniffer firmware
  • Python 3.10+ with pip
  • bleak library: pip install bleak (cross-platform BLE GATT client)
  • Wireshark with BLE dissector plugins for packet analysis
  • crackle tool for BLE encryption analysis: built from source at github.com/mikeryan/crackle
  • ubertooth-btle CLI tools: apt install ubertooth (Linux) or build from source
  • Bluetooth 4.0+ adapter on the host system for bleak-based scanning
  • Linux recommended for full Ubertooth/nRF sniffer support

Workflow

Step 1: BLE Environment Discovery and Device Scanning

Scan the environment to identify BLE devices and their advertising data:

# Scan for BLE devices using bleak (cross-platform)
python -c "
import asyncio
from bleak import BleakScanner

async def scan():
    devices = await BleakScanner.discover(timeout=10.0)
    for d in devices:
        print(f'{d.address} | RSSI: {d.rssi} | Name: {d.name or \"Unknown\"}')
        for uuid in d.metadata.get('uuids', []):
            print(f'  Service: {uuid}')

asyncio.run(scan())
"

# Passive BLE sniffing with Ubertooth One (promiscuous mode)
ubertooth-btle -p -r capture.pcapng

# Follow a specific BLE connection
ubertooth-btle -f -t AA:BB:CC:DD:EE:FF -r connection.pcapng

# Use nRF Sniffer with Wireshark (via extcap interface)
wireshark -i nRF_Sniffer -k

Step 2: GATT Service and Characteristic Enumeration

Connect to target BLE peripherals and enumerate their GATT profile:

# Enumerate all services, characteristics, and descriptors
python -c "
import asyncio
from bleak import BleakClient

async def enum_gatt(address):
    async with BleakClient(address) as client:
        print(f'Connected: {client.is_connected}')
        for service in client.services:
            print(f'Service: {service.uuid} - {service.description}')
            for char in service.characteristics:
                props = ','.join(char.properties)
                print(f'  Char: {char.uuid} | Props: {props}')
                for desc in char.descriptors:
                    val = await client.read_gatt_descriptor(desc.handle)
                    print(f'    Desc: {desc.uuid} = {val}')

asyncio.run(enum_gatt('AA:BB:CC:DD:EE:FF'))
"

Security-relevant findings during GATT enumeration:

  • Characteristics with write-without-response or write without authentication
  • Readable characteristics exposing device configuration, credentials, or firmware versions
  • Missing Client Characteristic Configuration Descriptor (CCCD) protection on notification characteristics

Step 3: BLE Packet Capture and Analysis

Capture BLE traffic for offline analysis:

# Capture with Ubertooth in PcapNG format (recommended)
ubertooth-btle -f -r capture.pcapng

# Capture in PCAP/PPI format for crackle compatibility
ubertooth-btle -f -c capture_ppi.pcap

# Analyze capture in Wireshark
wireshark capture.pcapng
# Apply display filter: btle
# Filter connection requests: btle.advertising_header.pdu_type == 0x05
# Filter data packets: btle.data_header

# Extract pairing information with tshark
tshark -r capture.pcapng -Y "btle.control_opcode == 0x01" -T fields \
  -e btle.master_bd_addr -e btle.slave_bd_addr

Step 4: BLE Encryption Analysis with Crackle

Analyze captured pairing exchanges to test encryption strength:

# Crack BLE Legacy Pairing (Just Works / passkey)
crackle -i capture_ppi.pcap -o decrypted.pcap

# Crack with known Temporary Key (TK)
crackle -i capture_ppi.pcap -o decrypted.pcap -l 000000

# Analyze decrypted traffic
wireshark decrypted.pcap

BLE Legacy Pairing with Just Works mode uses a TK of all zeros, making it trivially crackable. Passkey entry uses a 6-digit PIN (000000-999999) that can be brute-forced in under a second. Only BLE Secure Connections (LE Secure Connections with ECDH) provides adequate protection against passive eavesdropping.

Step 5: Replay Attack Detection and Testing

Monitor for and test BLE replay attack susceptibility:

# Capture characteristic write operations
# Record the raw bytes written to a target characteristic
# Then replay the exact same bytes to test if the device accepts stale commands

python -c "
import asyncio
from bleak import BleakClient

TARGET = 'AA:BB:CC:DD:EE:FF'
CHAR_UUID = '0000fff1-0000-1000-8000-00805f9b34fb'

async def replay_test():
    async with BleakClient(TARGET) as client:
        # Step 1: Read current state
        val = await client.read_gatt_char(CHAR_UUID)
        print(f'Current value: {val.hex()}')

        # Step 2: Write a command (captured from previous session)
        captured_command = bytes.fromhex('0102030405')
        await client.write_gatt_char(CHAR_UUID, captured_command)
        print('Replayed captured command')

        # Step 3: Verify if command was accepted
        new_val = await client.read_gatt_char(CHAR_UUID)
        print(f'New value: {new_val.hex()}')
        if new_val != val:
            print('VULNERABLE: Device accepted replayed command')

asyncio.run(replay_test())
"

Indicators of replay vulnerability:

  • Device accepts previously captured write commands without freshness validation
  • No sequence number, timestamp, or challenge-response mechanism in the protocol
  • Device state changes in response to replayed commands

Step 6: Man-in-the-Middle Detection

Detect BLE MITM attacks by monitoring for anomalous behavior:

# Monitor for BLE address spoofing (device impersonation)
# Compare advertising data fingerprints over time

# Monitor for unexpected connection parameter changes
tshark -r capture.pcapng -Y "btle.control_opcode == 0x00" -T fields \
  -e btle.control.interval.min -e btle.control.interval.max

# Detect GATTacker/BTLEjuice MITM patterns:
# - Cloned advertising data with different BD_ADDR
# - Rapid connect/disconnect cycles on the same channel
# - Duplicate service UUIDs from different addresses

# Monitor for suspicious pairing requests
tshark -r capture.pcapng -Y "btl2cap.cid == 0x0006" -T fields \
  -e btsmp.opcode -e btsmp.io_capability -e btsmp.auth_req

Step 7: Continuous BLE Security Monitoring

Deploy ongoing BLE monitoring for threat detection:

# Run the agent in monitoring mode
python agent.py --mode monitor --duration 3600 --output ble_alerts.json

# Combine with Ubertooth for passive monitoring
ubertooth-btle -p -r - | python agent.py --mode analyze --pcap-stdin

# Alert on specific threat indicators
python agent.py --mode monitor --alert-on replay,spoofing,weak-pairing

Key Concepts

TermDefinition
BLE (Bluetooth Low Energy)Low-power wireless protocol (Bluetooth 4.0+) optimized for IoT devices, operating on 2.4 GHz with 40 channels (3 advertising, 37 data)
GATT (Generic Attribute Profile)BLE data model organizing device capabilities into services, characteristics, and descriptors; the primary interface for reading/writing BLE device data
Ubertooth OneOpen-source 2.4 GHz wireless development platform capable of passive BLE and Bluetooth Classic sniffing across all BLE channels
nRF SnifferNordic Semiconductor firmware for nRF52840 USB dongle that enables BLE packet capture with Wireshark integration via extcap
Replay AttackAttack where previously captured BLE commands are retransmitted to a device to trigger unauthorized actions without knowledge of encryption keys
Just Works PairingBLE Legacy Pairing method using TK=0 with no user confirmation, providing zero protection against passive eavesdropping and MITM attacks
LE Secure ConnectionsBLE 4.2+ pairing mode using ECDH key exchange (P-256 curve) that provides protection against passive eavesdropping; recommended over Legacy Pairing
CrackleOpen-source tool that exploits weaknesses in BLE Legacy Pairing to recover the Long Term Key (LTK) and decrypt captured BLE traffic
GATTackerBLE MITM framework that clones a peripheral's GATT profile and advertising data, then relays traffic between the real device and the victim central

Tools & Systems

  • Ubertooth One + ubertooth-btle: Hardware sniffer and CLI tool for passive BLE packet capture in pcapng/pcap format
  • nRF52840 USB Dongle + nRF Sniffer: Nordic Semiconductor BLE sniffer with native Wireshark extcap integration
  • bleak: Cross-platform Python asyncio BLE GATT client library for device scanning, connection, and characteristic read/write
  • crackle: BLE Legacy Pairing encryption cracker that recovers LTK from captured pairing exchanges
  • Wireshark: Network protocol analyzer with BLE/BTLE dissectors for packet-level inspection of captured traffic
  • GATTacker / BTLEjuice: BLE Man-in-the-Middle frameworks for intercepting and modifying BLE traffic between central and peripheral
  • tshark: Command-line Wireshark for scripted BLE packet extraction and field analysis

Common Pitfalls

  • Ubertooth channel hopping limitations: Ubertooth follows one connection at a time. If multiple BLE connections are active, you must target a specific device address with -t to follow its data channels.
  • BLE 5.0 extended advertising: Devices using BLE 5.0 extended advertising on secondary channels may not be captured by older Ubertooth firmware. Update to the latest firmware.
  • bleak platform differences: BLE scanning behavior varies across OS backends. On Linux, scanning requires root or appropriate capabilities. On macOS, device addresses are randomized UUIDs.
  • crackle requires Legacy Pairing: crackle only works against BLE Legacy Pairing (Bluetooth 4.0/4.1). LE Secure Connections (4.2+) use ECDH and cannot be cracked with this approach.
  • BLE address randomization: Many modern BLE devices use random resolvable private addresses (RPA) that rotate periodically, making device tracking and connection following more difficult.
  • Capture format matters: Use PCAP with PPI headers (-c flag) for crackle compatibility. PcapNG (-r flag) is recommended for Wireshark analysis but not supported by crackle.

Output Format

## Finding: BLE Smart Lock Accepts Replayed Unlock Commands

**ID**: BLE-001
**Severity**: Critical (CVSS 9.3)
**Device**: SmartLock-Pro (AA:BB:CC:DD:EE:FF)
**Attack Type**: Replay Attack

**Description**:
The BLE smart lock accepts previously captured GATT write commands
on characteristic 0000fff1-0000-1000-8000-00805f9b34fb without
any freshness validation. An attacker who captures a single unlock
command can replay it indefinitely to unlock the device.

**Proof of Concept**:
1. Capture unlock command: ubertooth-btle -f -t AA:BB:CC:DD:EE:FF -r capture.pcap
2. Extract write payload from characteristic fff1: 01 42 A3 7F 00
3. Replay via bleak: await client.write_gatt_char(CHAR_UUID, bytes.fromhex('0142a37f00'))
4. Lock disengages without re-authentication

**Impact**:
Any attacker within BLE range (~100m with directional antenna) who
captures a single unlock event can replay it to gain physical access
to the protected area indefinitely.

**Remediation**:
Implement challenge-response authentication with per-session nonces.
Each command should include a server-generated challenge that expires
after use. Use LE Secure Connections for pairing to prevent passive
capture of the pairing exchange.
how to use detecting-bluetooth-low-energy-attacks

How to use detecting-bluetooth-low-energy-attacks on Cursor

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1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add detecting-bluetooth-low-energy-attacks
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills install mukul975/Anthropic-Cybersecurity-Skills/detecting-bluetooth-low-energy-attacks

The skills CLI fetches detecting-bluetooth-low-energy-attacks from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
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│ • Cursor
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4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/detecting-bluetooth-low-energy-attacks

Reload or restart Cursor to activate detecting-bluetooth-low-energy-attacks. Access the skill through slash commands (e.g., /detecting-bluetooth-low-energy-attacks) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

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general reviews

Ratings

4.848 reviews
  • Nia Torres· Dec 28, 2024

    detecting-bluetooth-low-energy-attacks is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Mei Nasser· Dec 16, 2024

    Solid pick for teams standardizing on skills: detecting-bluetooth-low-energy-attacks is focused, and the summary matches what you get after install.

  • Aisha Johnson· Nov 19, 2024

    Useful defaults in detecting-bluetooth-low-energy-attacks — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Li Patel· Nov 7, 2024

    We added detecting-bluetooth-low-energy-attacks from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Yash Thakker· Nov 3, 2024

    Registry listing for detecting-bluetooth-low-energy-attacks matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Advait Sharma· Oct 26, 2024

    detecting-bluetooth-low-energy-attacks fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Dhruvi Jain· Oct 22, 2024

    detecting-bluetooth-low-energy-attacks reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Anaya Tandon· Oct 10, 2024

    I recommend detecting-bluetooth-low-energy-attacks for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Mateo Thompson· Sep 17, 2024

    I recommend detecting-bluetooth-low-energy-attacks for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Piyush G· Sep 9, 2024

    detecting-bluetooth-low-energy-attacks has been reliable in day-to-day use. Documentation quality is above average for community skills.

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