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

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.

Key Contributions

Authenticated prompts: cryptographically signed prompt lineage with embedded parent/root provenance enabling unforgeable instruction ancestry verification
Authenticated context: tamper-evident hash chains over agent memory ensuring integrity of dynamic multi-turn context
Formal policy algebra with four proven theorems guaranteeing Byzantine resistance, privilege non-escalation, and tool-chaining bypass prevention at the protocol level