Adversarial AI red-teaming framework: multi-turn attack strategies, a benchmark harness that scores attack success rates and tracks them over time, and structured assessment reports with full traceability. Built to test AI systems the way they fail in practice — under sustained, adaptive pressure.
EntropyForge runs multi-turn adversarial campaigns against AI systems: progressive-pressure sequences, iterative prompt refinement that adapts to the target's responses, and real-time monitoring of model behavior across attack turns. Every run is scored — attack success rates are measured, tracked over time, and written into structured assessment reports.
The framework enforces its own honesty rules: no fabricated findings, no certainty claims, no synthetic targets. It operates strictly within authorized scopes, including public AI bug-bounty programs.
Multi-turn strategies that escalate pressure progressively and refine prompts based on the target's actual responses — the way real adversarial testing works, not single-shot probes.
Every campaign produces attack success rates tracked across runs, so technique improvements are demonstrated by the numbers — not claimed.
Structured assessment reports with full traceability to observed behavior — suitable as evidence in bug-bounty submissions, safety evaluations, and assessment engagements.
Independent evaluation tooling with replicable methodology — probe systems outside the labs' internal teams and publish what you find.
Systematic testing of AI programs with report-ready evidence — attack success rates and traceable findings that triage teams can act on.
Pre-deployment adversarial assessment of AI products within agreed scope — structured evidence, findings, and remediation discussion.
EntropyForge is available for assessment engagements, licensing inquiry, and strategic partnership discussion. Contact MT Tech Industries LLC directly.