Results for “frontier-models”

Detect poisoned training data and backdoored models across the ML pipeline using statistical analysis, activation clustering, and spectral signatures.

Migrates symbols and reverse-engineering results from an older binary version to a newer one using LLM-based structured diffing of disassembly and pseudocode, enabling rapid offset and function-name mapping when PDBs are missing.