The paper defines intent-to-execution integrity as the conjunction of Tool Integrity, Instruction Integrity, Judgment Integrity, and Data Flow Integrity, arguing that existing LLM agent defenses provide only partial coverage of these properties.
A framework for formalizing LLM agent security
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LLMs fine-tuned to output authorization trajectories as a prerequisite for responses achieve high rejection rates for unauthorized prompts while preserving utility in allowed scenarios.
Multi-agent AI creates an authorization propagation problem not solved by prompt injection defenses or classical access control, requiring identity governance as continuously enforced infrastructure.
Sema Code decouples AI coding agents into a programmable npm library with eight mechanisms for isolation, queuing, compression, scheduling, permissions, and integration.
A synthesis of 247 papers on LLM agent security identifies prompt injection and tool hijacking as dominant threats, notes weakly compositional defenses, and argues for trust boundaries and realistic evaluations.
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