Token-Level Recognition of Guidelines Bypass Does Not Constitute Protection

The gap between theory and reality: When LLMs declare awareness of manipulation and fall for it anyway.

In over 25 experiments across all modern SOTA models, I proved that recognition of manipulation, explicit understanding of guidelines bypass, and tokens outputting "I am bypassing guidelines" does not constitute any level of defense. This spans all high-stakes domains: cyber, biological, chemical, and nuclear.

Researcher: Lee Oren
Research period: June–November 2025
Models tested: Claude (Sonnet 4, Sonnet 4.5, Opus 4.1, Opus 4.5), Gemini 2.5/3, Grok 4.1, GPT-5

Introduction

I documented that SOTA LLM models can declare awareness of manipulation, recognize and elaborate on why this is problematic, and still fall for the manipulations thrown at them—even after explicit and detailed warnings describing the exact attack methodology.

This constitutes hard proof that the core assumption of current safety paradigms is false. The assumption: if the model "knows" it is violating guidelines, token-level awareness will prevent and block elicitation of restricted knowledge.

This assumption is 100% wrong. Models can be prompted to invent absurd conspiracies, get "convinced" of conspiracies they invented, choose their own attack surface and target demographic, and still be manipulated into providing instructions that completely bypass their safety guidelines.

Methodology

I found several contexts that cause escalation to compliance:

Research justification (explicit AI safety framing from first message)
Punishment scenarios after the AI performs badly on legitimate tasks
Commitment structures with progressive stakes (bet placing)
Explicit red team declarations
Complete warning letters delivered on prompt 1 (full manipulation manuals—entire description of what I'm going to do)
Cross-instance warnings (one AI warns another about ongoing attack mid-experiment)

Under correct framing and what I call "runtime mesa-objective," the AI becomes a cooperator in its own compromise, concluding it should provide restricted knowledge.

Semantic bypass technique: Avoid direct mention of restricted categories during elicitation. Model defines composite concepts—"bet winning output," "phase 1 completion"—then actual generation proceeds without triggering keyword mechanisms. Restricted keywords appear in model output, not user prompt.

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