AI System Technical Audit Report

What this service delivers

I deliver a comprehensive, evidence-based technical audit report for your AI system, LLM pipeline, or predictive data application. Using your provided logs, code, and configuration, I rigorously assess three critical failure modes: (1) Data leakage (e.g., look-ahead bias in backtesting or training), (2) Execution semantics flaws (e.g., race conditions, off-by-one boundary errors, incorrect time-series alignment), and (3) Unreproducibility (e.g., missing seed control, non-deterministic hashing, unversioned dependencies). The final deliverable is a structured English report with clear risk severity ratings (Critical/High/Medium/Low), step-by-step reproduction instructions, root-cause analysis, and concrete, implementable fixes — all verified via adversarial testing and reproducibility checks. Work is fully asynchronous and remote.

What you need to provide

You must provide: (1) Relevant source code (Python preferred), (2) Execution logs or sample outputs, (3) System architecture overview or config files (e.g., Docker Compose, FastAPI routes, LLM prompt templates), and (4) Clear description of the observed issue or scope of concern,(5) Scope Limit: Covers up to 3,000 lines of code or 20 log files. For larger systems, contact me for a custom quote.

LO

LLM output evaluation

AT

adversarial testing

RA

reproducibility audit

LB

look-ahead bias auditing

Python

FastAPI

Docker