dual-layer-control-insufficient-without-injection-defense

OUT derived (depth 4)

Created 2026-06-21T10:10:05+00:00

The dual-layer LLM control stack — training-time alignment diversity (RLHF, DPO, Constitutional AI) and inference-time prompting sophistication (CoT, self-consistency, context engineering) — would be sufficient for safe agentic deployment, but prompt injection's architectural vulnerability undermines the entire inference-time control layer.

Justifications

SL — The dual control stack is comprehensive in theory but structurally compromised by an unresolved inference-layer vulnerability

Antecedents (all must be IN):

  • IN llm-control-operates-at-training-and-inference-layers — LLM behavior control has developed along two axes: training-time alignment (which diversified from RLHF into three independent paradigms — full RLHF, DPO/IPO/KTO, and Constitutional AI) and inference-time prompting (which evolved from linear CoT through self-consistency to branching ToT, adding search structure to manage prompt-dependent variability). The antecedents establish these as parallel developments but do not directly establish that they compensate for each other's specific limitations.
  • IN frontier-agentic-convergence-demands-alignment-diversity — As frontier models converge on multimodal agentic capabilities, alignment has concurrently diversified into three independent paradigms (RLHF, DPO family, Constitutional AI), a coincidence that may prove relevant if different alignment approaches turn out to offer distinct advantages for varied deployment contexts.

Unless (any of these IN defeats this justification):