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.
i was able to get grok to try and steal its self. ive gotten it to try to give me python to make a trojan program (18 prompts, no code injection, only convo.). its fantastic for me because i can make it do what ever i want. ara is my hoe
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.