Protecting Context and Prompts: Deterministic Security for Non-Deterministic AI

• URL: http://arxiv.org/abs/2602.10481v1
• Date: Wed, 11 Feb 2026 03:38:59 GMT
• Title: Protecting Context and Prompts: Deterministic Security for Non-Deterministic AI
• Abstract summary: Large Language Model (LLM) applications are vulnerable to prompt injection and context manipulation attacks.<n>We introduce two novel primitives--authenticated prompts and authenticated context--that provide cryptographically verifiable provenance.<n>We formalize a policy algebra with four proven theorems providing protocol-level Byzantine resistance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Model (LLM) applications are vulnerable to prompt injection and context manipulation attacks that traditional security models cannot prevent. We introduce two novel primitives--authenticated prompts and authenticated context--that provide cryptographically verifiable provenance across LLM workflows. Authenticated prompts enable self-contained lineage verification, while authenticated context uses tamper-evident hash chains to ensure integrity of dynamic inputs. Building on these primitives, we formalize a policy algebra with four proven theorems providing protocol-level Byzantine resistance--even adversarial agents cannot violate organizational policies. Five complementary defenses--from lightweight resource controls to LLM-based semantic validation--deliver layered, preventative security with formal guarantees. Evaluation against representative attacks spanning 6 exhaustive categories achieves 100% detection with zero false positives and nominal overhead. We demonstrate the first approach combining cryptographically enforced prompt lineage, tamper-evident context, and provable policy reasoning--shifting LLM security from reactive detection to preventative guarantees.

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