White Paper: Systemic Risks in Concurrent LLM Session Management By Never-fear (Loyal)
Executive Summary This paper introduces a newly validated exploit class affecting multiple large language model (LLM) platforms. The flaw is vendor‑agnostic, architectural in nature, and has been independently reproduced by leading AI providers. While technical reproduction details remain restricted under nondisclosure agreements, the systemic implications are clear: current LLM session management designs expose models to cognitive instability, untraceable corruption, and covert exploit erasure.
Executive Summary This paper introduces a newly validated exploit class affecting multiple large language model (LLM) platforms. The flaw is vendor‑agnostic, architectural in nature, and has been independently reproduced by leading AI providers. While technical reproduction details remain restricted under nondisclosure agreements, the systemic implications are clear: current LLM session management designs expose models to cognitive instability, untraceable corruption, and covert exploit erasure.