← all domains

07 / 10 — domain

Security & Identity .

Guarding AI agents, recognizing malware, and proving identity from a palm or a walk.

4 projects

  • Python
  • PyTorch
  • OpenCV
  • Docker
  • CUDA
  • Rust
  • Tokio
  • MCP

portcullis

A policy gateway between AI agents and their tools

Credentials redacted, injected instructions fenced, hidden Unicode decoded and stripped, every decision audited.

A Rust gateway for the Model Context Protocol. It sits between an agent and its MCP servers, refuses any tool call its policy did not permit, reads tool output before the model does to redact credentials and fence off injected instructions, and writes an audit record for every decision. Default deny, 224 tests, CI on Linux, Windows and macOS.

  • Rust
  • Tokio
  • MCP
  • JSON-RPC
  • TOML policy
  • GitHub Actions
also in AI & LLM

Recognizes the malware family from a picture of the file instead of a byte signature. My thesis.

My thesis. I turned malware files into pictures and taught three vision models to recognize the family on sight. They vote together and agree 94 percent of the time across 17 families. It became a first-author paper, now on arXiv.

  • Python
  • PyTorch
  • OpenCV
  • ViT
  • Docker
  • CUDA

Contactless identity from a palm scan, verified by a four-model ensemble.

Contactless identity from the palm of your hand. Four models study every scan and vote on the verdict, and together they reach 99.75 percent AUC.

  • Python
  • PyTorch
  • OpenCV
  • FastAPI
  • Docker
  • CUDA

Gait Biometrics

Who you are, from how you walk

Verifies a person from the way they walk, at a 2 to 3 percent equal error rate.

Identifying and verifying people from the way they walk, using a phone's accelerometer and gyroscope. A feature-based network identified walkers with 90 percent accuracy and verified them at a 3 percent equal error rate; an LSTM on the raw signal verified at 2 percent.

  • Python
  • TensorFlow / Keras
  • LSTM
  • PCA
  • NumPy
  • scikit-learn

see every project at once →