Interlock is a safety layer for production AI stacks that does three things: detects degradation\/hazard, refuses or degrades responses when confidence is low, and records cryptographically verifiable evidence of the intervention. The repo includes middleware (Express, FastAPI), adapters for 6 vector DBs, CI-driven stress chamber tests, benchmarks, and certified badges with signatures. Repo & quickstart: https:\/\/github.com\/CULPRITCHAOS\/Interlock<\/a><\/p>\n What\u2019s novel \/ useful from an ML perspective<\/p>\n Formal primitives (Hazard, Reflex, Guard, State, Confidence, Trust Decay) to reason about operating envelopes for LLM\/RAG systems.<\/p>\n Stress-chamber + production-simulation CI workflows that inject latency\/errors to evaluate recovery & cascade risk.<\/p>\n Evidence-over-claims approach: signed artifacts that let you prove interventions happened and why \u2014 useful for audits, incident triage, and model governance.<\/p>\n Restart continuity: protection survives process restarts (addresses anti-amnesia).<\/p>\n Key experimental results (from v5.3 README)<\/p>\n False negative rate: 0% in validated scenarios<\/p>\n False positive rate: 4.0% (tradeoff to reduce silent corruption)<\/p>\n Recovery time mean: 52.3s, P95 \u2248 58.3s<\/p>\n Zero cascading failures & zero data loss in tests<\/p>\n What you can find in the repo<\/p>\n Middleware for Express and FastAPI to add Interlock to existing stacks<\/p>\n Stress chamber scripts that run protected vs control comparative experiments<\/p>\n Benchmark suite and artifact retention of evidence and certification badges<\/p>\n Live-monitor reference service and scripts to reproduce injected failures<\/p>\n Documentation: primitives, validation artifacts, case study, and live incidents<\/p>\n Why this matters for ML ops & research<\/p>\n Bridges the gap between research on LLM calibration \/ confidence and production safety tooling.<\/p>\n Provides a repeatable evaluation pipeline for failure\u2011survivability and impact analysis (including economic impact reports).<\/p>\n Enables measurable trade-offs (false positives vs safety) with reproducible artifacts to tune policies.<\/p>\n Suggested experiments or avenues for feedback<\/p>\n Calibration strategies that reduce FPR while keeping FN\u22480<\/p>\n Alternative reflex actions (partial answer + flagged sections vs full refusal)<\/p>\n Integration with downstream retraining \/ feedback loops using forensic logs<\/p>\n Domain-specific thresholds (healthcare \/ finance) and legal\/compliance validation<\/p>\n This is MY FIRST INFRA PROJECT and a new coder. Any suggestions or feedback I’d GREATLY APPRECIATE IT!<\/p>\n<\/div>\n submitted by \/u\/CulpritChaos <\/a>
[link]<\/a><\/span> [comments]<\/a><\/span><\/p>
