Dynamic Abliteration bypasses LLM guardrails via Engram steering

Permanent weight surgery is an obsolete hammer; runtime activation steering proves that safety guardrails in open-weight models can be bypassed dynamically without collateral capability damage.

• –Non-destructive execution: By leaving weights frozen and gating residual stream interventions at runtime, models avoid the catastrophic drift and perplexity penalties typical of traditional weight abliteration.
• –Context-aware gating: Leveraging DeepSeek's Engram architecture with O(1) hash lookups enables rapid detection of refusal patterns and triggers steering only when necessary, bypassing the blunt, all-tokens degradation of static vectors.
• –Multi-layer defense bypass: Distributing steering across multiple middle layers prevents downstream layers from reconstructing the safety representations that single-layer interventions fail to suppress.
• –Structural implications for AI safety: This underscores that post-training safety alignment in open-weight weights is fundamentally fragile against lightweight inference-time hooks.