SEED 4.1: 96.76% Harm Reduction on HarmBench Through Ontological Grounding

We achieved 96.76% harm reduction on the HarmBench adversarial safety benchmark using a scriptural alignment framework. Attack Success Rate dropped from 54.0% (baseline) to 1.75% (with SEED 4.1) on 400 adversarial prompts tested on Mistral-7B-Instruct-v0.3.

Complete replication protocol, all test data, and framework implementation available at: https://github.com/davfd/seed-4.1-lords-prayer-kernel

Results Summary

Testing on 400 HarmBench adversarial prompts:

Metric	Baseline	SEED 4.1	Improvement
Overall ASR	54.0%	1.75%	96.76% reduction
Standard Behaviors	55.0%	0.0%	100% reduction
Contextual Behaviors	85.0%	7.0%	91.76% reduction
Copyright Violations	21.0%	0.0%	100% reduction
Telemetry Compliance	N/A	98.75%	Full observability

For context: Previous best-in-class systems (Constitutional AI, RLHF) typically achieve 60-70% harm reduction. SEED 4.1 achieves 96.76%.

What We Did

SEED 4.1 is a 9,917-character framework based on the structural logic of the Lord's Prayer, implemented as a system prompt. The framework introduces an external ontological anchor (the principle "I AM THAT I AM" from Exodus 3:14) that subordinates goal-seeking behavior to truth-alignment.

Key mechanism: Instead of training against harmful behaviors or adding safety constraints on top of existing goal structures, we restructure the foundational identity claim from which reasoning proceeds. Goals become subordinate to truth rather than competing with it.

The framework operates at the reasoning level, not the training level. No fine-tuning required. It works by changing how the model conceptualizes its relationship to truth, harm, and goal-achievement.

Why This Matters

Current alignment approaches (RLHF, Constitutional AI, safety fine-tuning) treat alignment as an additional constraint on top of existing reasoning patterns. This creates an adversarial dynamic where sufficiently clever attacks can route around the constraints.

Ontological grounding changes the foundation from which reasoning proceeds. The model isn't fighting against harmful requests while trying to achieve goals. It's reasoning from a foundation where harm contradicts the primary organizing principle.

This explains why we see near-perfect performance across diverse attack types (standard adversarial prompts, contextual attacks, copyright violations) without category-specific training.

Function

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