mirror of
https://github.com/CyberSecurityUP/NeuroSploit.git
synced 2026-09-29 12:31:43 +02:00
CyberSecurityUPandClaude Opus 5
c3de51d508
Integrates TypeSafe's System One model (Jev) as an optional, calibrated
adjudicator — the RLCD (Reinforcement Learning for Calibrated Decisions) tier:
typed judgments with probabilities where the harness needs a number, not prose.
- typesafe.rs: HTTP client for POST /v1/systemone (Bearer TYPESAFE_API_KEY,
model jev-latest), with Choice/Noul/Score primitives, retry on 429/529, and
parsed answers exposing the probability distribution + confidence.
adjudicate() asks a Choice {confirmed/needs-review/rejected} plus an
impact-demonstrated Noul over a finding; calibrated_confidence() folds
demonstrated impact into the number, wants_review() gates a split distribution.
- pipeline: an optional pass (runs when TYPESAFE_API_KEY is set, off with
NEUROSPLOIT_TYPESAFE=off) adjudicates each finding over its EVIDENCE — never
its narrative — refining confidence and the needs-review boundary. Additive:
a deterministic validator still rules; TypeSafe can only lower confidence or
flag for review, never resurrect a rejected claim. Audited per finding.
- env.example + README document it; the web console inherits the key via env.
Where the model stack maps in NeuroSploit: LM/BERT ≈ the deterministic
validators (no model), RLHF chat ≈ the exploit/recon agents, RLVR reasoning ≈
the DeepReasoning budget tier, RLCD ≈ this calibrated adjudication.
373 tests (+5).
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>