Immutable checkpoint storage for ML training pipelines. Kernel-level protection, anomaly detection, score-gated rollback, and self-healing recovery. Built in Rust.

Static analysis and integrity verification for GGUF model files

Machine-checks every fixed model artefact—weights, vocab, quant tables, tokenizers.

Detect and defend against AI model poisoning attacks on ML training data

Heuristic signals on whether an OpenAI-/Anthropic-compatible endpoint serves the model it claims — catch model-swapping, quantization & silent context truncation. Zero-dependency Python CLI. Signals, not proof.

Practical guardrails against silent GPU-side model corruption

Blockchain-based ML model and file integrity verification using SHA-256, Proof-of-Work, and IPFS via Pinata

Time‑Shift LLM Integrity Tester

Security scanner for the LLM fine-tuning lifecycle — detect dataset poisoning, malicious LoRA adapters, and model weight tampering

Live-state attestation and drift detection for secure AI inference runtimes

Model integrity and provenance verification for LLMs and AI models. Generate, verify, and cryptographically secure your model artifacts.