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.
Offline-first evidence and verification framework for AI model artifacts, transformations, runtime identity, and provenance.
Machine-checks every fixed model artefact—weights, vocab, quant tables, tokenizers.
Static analysis and integrity verification for GGUF model files
Detect and defend against AI model poisoning attacks on ML training data
Blockchain-based ML model and file integrity verification using SHA-256, Proof-of-Work, and IPFS via Pinata
Synthetic finance-agent model integrity fixtures from LeChiffre AI.
Evidence-led AI API model integrity ranking for metadata consistency, blinded behavior checks, anomaly review, and community PRs.
Time‑Shift LLM Integrity Tester
Model integrity and provenance verification for LLMs and AI models. Generate, verify, and cryptographically secure your model artifacts.
Live-state attestation and drift detection for secure AI inference runtimes
Practical guardrails against silent GPU-side model corruption
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.
Security study of an AI medical decision-support prototype, including attack scenario analysis, model-swap simulation, SHA-256 integrity protection, security alerts and ethical considerations.
Security scanner for the LLM fine-tuning lifecycle — detect dataset poisoning, malicious LoRA adapters, and model weight tampering
The Applied AI Universe Coding Guide: Adversarial Defenses
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