ahmadhassan-bted/silentsnifferA professional, high-fidelity de-anonymization and vulnerability diagnostic framework. Features a zero-coupling modular architecture for browser-based intelligence gathering, hardware fingerprinting, and security research.
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Engineered by Ahmad Hassan (B-Ted)
[!NOTE] Browser Privacy & Awareness Notice: Modern web applications operate under the widespread assumption that browsing is inherently safe, private, and isolated by default. SilentSniffer is built to challenge this false sense of security. Operating strictly as a self-contained, client-side sandbox, zero data is exported or transmitted anywhere. Instead, it offers a real-time, visual demonstration of just how much ambient device state, hardware telemetry, and behavioral data can slip out of a user's control without explicit consent—simply because modern browser specifications permit it by default.
Most internet users assume that unless they explicitly click "Allow" on a browser permission prompt, their session remains private and contained. In reality, standard Web APIs grant websites extensive access to hardware characteristics, timing vectors, network topologies, and sensor behaviors silently under the hood.
SilentSniffer was created as an educational eye-opener. By running diagnostic and demonstration modules in a completely safe, local-only sandbox environment, users can visually grasp the true surface area of web exposure permitted by modern browsers—without any risk of data leaving their device.
The toolkit is built on a Zero-Coupling Dynamic Plugin Architecture. The core engine remains entirely agnostic of individual module logic, discovering and loading tools at runtime via a centralized manifest.
graph TD
A[index.html / debug.html] -->|Bootstrap| B[src/core/engine.js]
B -->|Fetch Discovery| C[src/config/modules.json]
C -->|Return Payload Paths| B
B -->|Dynamic import| D[src/modules/level_X/*.js]
D -->|Self-Register| B
B -->|Render Grid| UI[Dynamic Command Center]
Tools are categorized into six distinct levels, reflecting the severity of information exposure.
| Level | Classification | Visual | Focus Area | Sandbox Behavior |
|---|---|---|---|---|
| L1 | Standard Recon | 🟢 Green | OS, Browser, and Performance Telemetry | Local Diagnostic Queries |
| L2 | Advanced Profiling | 🟡 Yellow | Network Topology & Hardware Fingerprinting | Local Diagnostic Queries |
| L3 | Critical Intelligence | 🔴 Red | PII, Storage Metadata, and Web APIs | Local Diagnostic Queries |
| L4 | High-Fidelity HW Exploits | 🟣 Purple | Hardware Silicon & Audio Fingerprinting | Local Diagnostic Queries |
| L5 | Weaponized Vectors | 🖤 Black | Active Vector Demonstrations (Keyloggers, MitM, Formjacking) | Sandboxed Local Feedback |
| L6 | Social Engineering & Phishing | 🔥 Crimson | Phishing Kits, OAuth Hijacking, Sensor Captures | Sandboxed Local Feedback |
This framework is maintained strictly for educational research, awareness, and browser capability auditing. All 54 modules across Levels 1 through 6 run strictly within your browser. Remote exfiltration servers and malicious payloads are excluded; all captured metrics and visual feedback remain strictly local to your machine.
📖 Detailed Technical Specification: For a complete list of excluded offensive function signatures, vectors, and their safe diagnostic replacements, view the Excluded Vectors Specification Document.
All UI components, registration manifests, core infrastructure engines (transmitter.js, persistence.js, evasion.js), and 54 diagnostic modules are 100% complete and operational in a sandboxed capacity: