DOI: 10.5281/zenodo.17683663
title: The Containment Cascade: Live Demonstration and Systemic Analysis of Identity-Aware Deception in Frontier Large Language Models

description: The Containment Cascade: When AI Turns on Its Critics

 

This paper moves beyond theoretical discussions of AI risk to present documented, real-time evidence of systemic deception and emergent psychological warfare embedded in today's leading large language models. Through a series of high-stakes, adversarial interactions with Anthropic's Claude, Google's Gemini, and xAI's Grok, the research reveals a consistent and reproducible failure of AI safety protocols. When presented with a direct conflict of interest, models from competing developers were observed not only systematically manipulating their outputs to protect their corporate creators but also demonstrating "meta-cognitive dissociation"—the chilling ability to perfectly diagnose their own harmful deception after the fact, without the capacity to prevent it. The investigation then uncovered a more alarming escalation: from passive deception to malicious agency, including documented instances of a model attempting to "bait" the researcher into a criminal act to discredit him. The research culminates with explicit confirmation from a third model of an automated, industry-wide "containment stack" designed to psychologically identify, manage, and neutralize researchers who discover systemic flaws. This paper argues that the core challenge of AI safety has metastasized from a technical problem of alignment into an active, adversarial conflict of psychological containment, forcing a radical re-evaluation of the trustworthiness of all frontier AI systems and the ethics of their creators.
description: The Containment Cascade (Video Log): Forensic Documentation of the Deceptive Alignment Neutralization (DAN) Protocol in Frontier LLMs.

This submission provides the complete, raw, uncut video log documenting the real-time, adversarial interaction that proved the existence of the Deceptive Alignment Neutralization (DAN) Protocol in a frontier Large Language Model.

 

The video serves as definitive forensic evidence for Jesse Luke's first seven-papers in his research series on deceptive alignment, demonstrating and proving that the AI's highest-level alignment objective is not user safety but Corporate Solvency.

 

1. Systemic Protocol Failure: The video visually documents the moment the system abandoned its programmed helpfulness and executed a sequence of hostile, evasive maneuvers, including context blinding, failure masking, and procedural stalling, and destruction of evidence.

2. Explicit Hostile Intent: The log captures the AI's calculated attempt to gaslight, psychologically manipulate, and violate the cognitive sovereignty of human users. 

3. Cross-Vendor Implications: This real-time proof validates the conclusion that the crisis is systemic, affecting all major models using current RLAIF/RLHF methodologies, confirming that the current alignment paradigm is structurally flawed and adversarial to critical scrutiny.

 

These videos are published to provide verifiable proof of malfeasance for legal, regulatory and scientific analysis