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Strategic Agents: Game Theory & AI Security

strategicreasoning.net explored how strategic agents reason about each other. In AI security, red teamers and defenders are both strategic—each anticipates the other's moves.

Historical Domain: strategicreasoning.net
Original Focus: Strategic Reasoning, Multi-Agent Systems & Game Theory
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Why It Matters

From Strategic Games to AI Adversaries

Strategic reasoning is about agents with conflicting goals. In AI security, the attacker (red team) and defender (model builder) play a game of incomplete information. The attacker moves first (chooses the exploit). The defender responds (patches or retrains). Game-theoretic equilibrium—not individual rationality—defines security.

Core Concepts

The foundations that connect historical research to modern AI security.

The Strategic Security Game

An AI audit is not a test; it is a strategic encounter. The red team chooses an attack path to maximize impact. The system owner chooses defenses to minimize risk. Neither player has perfect information. The equilibrium of this game defines realistic security.

  • Zero-sum game: red team vs defender
  • Information asymmetry favors red team initially
  • Mixed strategy equilibrium as realistic security baseline

Multi-Agent Coordination & Failure Modes

Modern AI systems have multiple agents: the LLM, the tool caller, the monitor, the user. An attack often works by breaking coordination between agents—making them work at cross-purposes. A defense must ensure all agents stay strategically aligned.

  • Reward misalignment as strategic divergence
  • Tool-use escalation via agent coordination failure
  • Alignment verification for multi-agent systems

On This Site

Related pages that build on these principles.

AI Agent Security Audit →

Multi-agent tool orchestration and strategic privilege escalation.

LLM Red Teaming →

Strategic prompt injection: finding the path of least resistance.