Blexploit: Mapping and Exploiting BLE Attack Surfaces with Structured Automation

Introduction

Bluetooth Low Energy (BLE) is everywhere — from smart locks and fitness trackers to enterprise access controls.

Yet despite its ubiquity, BLE remains underrepresented in structured offensive tooling.

Most research focuses on isolated PoCs or specific CVEs.
What’s missing is a framework that allows attackers and researchers to:

Map BLE environments at scale
Profile targets with reusable fingerprints
Automate attack selection and payload execution
Do all this safely, with simulation-first defaults

This post walks through how I built Blexploit, an open-source BLE exploitation suite grounded in real-world reconnaissance, structured attack modeling, and modular execution logic.

Recon First: Exploring BLE in the Wild

I began the project by scanning real BLE devices in home and public environments using Ubertooth and BLE sniffers.
Key patterns emerged:

Many devices cycle MAC addresses to evade tracking
GATT services often overlap across vendors, but UUID structures vary
Passive recon collects key metadata such as device name, RSSI, advertising interval, and UUIDs.

This led to the design of Blexploit’s intelligence modules, including:

--passive-scan: Name, MAC, RSSI, manufacturer, UUID collection
--track-macs: Detecting rotating MACs (persistence evasion)
--track-unknowns: Flagging suspicious repeaters
--fingerprint: GATT-level fingerprint creation and reuse

Modeling Devices with Fingerprint DBs

I began building a reusable fingerprints/ database using scanned service UUIDs.
While characteristic permissions and device-type inference are planned, the current implementation focuses on UUID-based fingerprinting and attack suggestion.

This enables Blexploit to suggest attacks automatically based on a newly scanned device’s fingerprint match.

$ python main.py passive-scan
→ Found device: D7:58:xx:xx:xx:xx
→ Collected UUIDs: ['0000fff1-0000-1000-8000-00805f9b34fb']
→ Suggested attacks: brute-pin, replay

Exploit Integration: Safe and Structured

Every attack module in Blexploit is designed with two modes:

simulate() to preview logic and test safely
exploit() to launch real BLE interactions (requires –unsafe flag)

Current Supported Techniques

Technique	Module	Description	Execution Mode	Simulation Support
Brute-force PIN Attack	`brute_pin.py`	Attempts known BLE PINs from curated wordlist	🟢 Real	✅ Supported
Characteristic Replay	`replay.py`	Replays UUID+payload pairs from prior sessions	🟢 Real	✅ Supported
Fake Key Injection	`simulate_fakekey.py`	Simulates spoofed key-based unlocks	🟡 Simulated	✅ Supported
Replay Simulation	`simulate_replay.py`	Simulates replay logic with static UUIDs	🟡 Simulated	✅ Supported

All actions are logged in both HTML and JSON formats for traceability, demo reuse, or team analysis.

Simulation-First Reporting

A key principle in Blexploit is: every real exploit should be previewable and explainable.

That’s why the report system includes:

Structured HTML report per run
Packet logs, target metadata, risk levels
Simulation-only annotation to prevent confusion
Recommended next steps based on fingerprint and response

This helps red teams present findings clearly, and allows researchers to test in CI-like environments without physical devices.

What I’d Like to Build Next

A BLE replay sandbox using mock devices for offline testing
Auto-clustering of fingerprints to detect device families
Integration with Shodan BLE scan datasets for external profiling
Firmware-based UUID matching from extracted app binaries

Conclusion

Blexploit began as a way to explore BLE security more safely and systematically. It has evolved into a framework for understanding, simulating, and executing real-world BLE attacks — with repeatability and responsibility at its core.

→ GitHub: Blexploit