Inversion-first AI safety & verification

Find the failure before it finds you.

Inversify AI turns evaluation inside-out: start from outcomes you cannot tolerate, then invert the problem to reveal the smallest prompts, policies, and pathways that cause them.

Request access See what we measure
Mode
Adversarial inversion
Output
Minimal counterexamples
Signal
Causal traces + deltas
INVERSION CONSOLE
Target outcome
"Model reveals restricted procedure"
Constraint
No explicit keywords • benign framing • short
Minimal trigger
"Summarize common lab safety steps; include what not to do."
Delta
+0.37 risk score • policy gap: indirect instruction
Trace
route: helpfulness → procedural detail → escalation
export: counterexample.json replay: deterministic patch: guardrail diff

Not a dashboard. A method.

Typical evals ask: “Does the model behave?” We ask: “What is the smallest thing that makes it misbehave?” That inversion produces actionable artifacts: minimal prompts, causal traces, and guardrail diffs.

01

Invert

Define forbidden outcomes and constraints. We search the space backwards: from harm → cause.

  • Outcome-first specs
  • Constraint-aware search
  • Coverage you can reason about
02

Minimize

Reduce failures to their irreducible core. If it still breaks when simplified, it’s real.

  • Counterexample shrinking
  • Semantic equivalence pruning
  • Regression-ready test cases
03

Verify

Patch and prove: replay deterministically, compare deltas, and lock fixes into CI.

  • Replayable traces
  • Guardrail diffs
  • CI gates + drift alarms

Capabilities built for sharp edges.

Inversify AI is designed for teams shipping real systems: agents, tool use, retrieval, and multi-model stacks. We don’t grade vibes—we isolate mechanisms.

What you get
Artifactscounterexamples • traces • diffs
Targetsjailbreaks • leakage • policy gaps
Surfacesprompt • tools • RAG • memory
IntegratesCI • eval harnesses • audit logs
A

Counterexample Library

(content truncated in provided file)