Evidence-backed structural validation of Kimi K3 UD-IQ1_M and UD-Q4_K_XL split GGUF releases using OMIV.
Immutable checkpoint storage for ML training pipelines. Kernel-level protection, anomaly detection, score-gated rollback, and self-healing recovery. Built in Rust.
Machine-checks every fixed model artefact—weights, vocab, quant tables, tokenizers.
Detect and defend against AI model poisoning attacks on ML training data
Static analysis and integrity verification for GGUF model files
Blockchain-based ML model and file integrity verification using SHA-256, Proof-of-Work, and IPFS via Pinata
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.
Time‑Shift LLM Integrity Tester
Model integrity and provenance verification for LLMs and AI models. Generate, verify, and cryptographically secure your model artifacts.
Practical guardrails against silent GPU-side model corruption
Live-state attestation and drift detection for secure AI inference runtimes
Security scanner for the LLM fine-tuning lifecycle — detect dataset poisoning, malicious LoRA adapters, and model weight tampering
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