Countermind: A Multi-Layered Security Architecture for Large Language Models

Abstract

The security of Large Language Model (LLM) applications is fundamentally challenged by "formfirst" attacks like prompt injection and jailbreaking, where malicious instructions are embedded within user inputs. Conventional defenses, which rely on post hoc output filtering, are often brittle and fail to address the root cause: the model's inability to distinguish trusted instructions from untrusted data [1]. This paper proposes Countermind, a multi-layered security architecture intended to shift d

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@misc{schwarz2025countermind,
  title = {{Countermind: A Multi-Layered Security Architecture for Large Language Models}},
  author = {Dominik Schwarz},
  year = {2025},
  month = oct,
  doi = {10.36227/techrxiv.175994550.08962082/v1},
  url = {https://doi.org/10.36227/techrxiv.175994550.08962082/v1}
}