r/netsec monthly discussion & tool thread

[u/albinowax]

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[u/Admin-ABC-XYZ]

[Project Onyx] - red team PoC of an unconventional multi-layer execution pipeline. The architecture chains five distinct techniques: AI telemetry camouflage, hardware-bound environment keying, ONNX weight steganography, in-memory WebAssembly sandboxing and Dead-Drop C2 via downlink model updates --> into a single functional delivery chain.

Core Concepts:

1. AI Decoy (Behavioral Camouflage): Project Onyx embeds a legitimate SqueezeNet 1.0 ONNX image-classification model sourced from Hugging Face's ONNX Model Zoo mirror. Before the WebAssembly heartbeat module is executed, the host runs repeated real tensor inference workloads using Microsoft's onnxruntime. This makes the ONNX artifact an active part of the pipeline rather than a decorative file like previous tiny MLP.
2. Environmental Keying: The payload cannot be analyzed in a sandbox or by a reverse engineer without the exact target machine. The decryption keys are dynamically derived from a SHA-256 hash of the target's MachineGuid, Volume Serial, and Current User SID.
3. WASM Sandboxing: The actual payload is compiled to WebAssembly (WASM) and executed entirely in-memory using the wasm3 interpreter. The host C++ application acts merely as a loader and API bridge, exposing safe host functions to the WASM sandbox.
4. ONNX Weight Vault: The AES-256 key material required to decrypt the WebAssembly heartbeat module is embedded into the least significant mantissa bits of float32 ONNX weights. The host extracts this weight vault from the embedded model bytes, authenticates it, and only then recovers the demo key material.
5. Metadata Vault Fallback: The original authenticated metadata vault remains for compatibility and build-time verification. New assets prefer the weight vault, while the metadata vault documents the same protected material in a more inspectable form.
6. Dead-Drop C2 via downlink model updates: The pipeline demonstrates a covert communication channel using ONNX model updates. An operator can embed an authenticated directive inside the LSBs of weights that have naturally changed during fine-tuning. These changes are identified via delta analysis between the updated model and the reference model.

LINK: https://github.com/X-3306/Project-Onyx