Game theory models strategic interactions between rational actors. In AI safety, the relevant actors are the AI agent, the attacker attempting to subvert it, and the safety system trying to prevent harm. Modeling these interactions as games reveals optimal strategies for both attackers and defenders, helping safety engineers design systems that are robust against strategic adversaries.
The simplest model is a two-player game between an attacker and a safety system. The attacker chooses an attack strategy (prompt injection variant, evasion technique, social engineering approach). The defender chooses a detection strategy (scanning rules, behavioral thresholds, policy configuration). The payoff depends on whether the attack succeeds or is caught.
A Nash equilibrium is a pair of strategies where neither player can improve their outcome by changing strategy unilaterally. For safety systems, finding the Nash equilibrium reveals the best detection strategy given that the attacker is also optimizing their approach.
If the defender uses a single fixed detection rule, the attacker optimizes around it. The equilibrium involves the defender randomizing across multiple detection strategies, making it harder for the attacker to predict which evasion will work.
In Stackelberg games, the defender commits to a strategy first, and the attacker observes and best-responds. This model applies when the safety system's configuration is partially observable (published policies, open-source detection rules). The optimal defender strategy accounts for the attacker's ability to study and adapt.
This is directly relevant to Authensor because policies may be stored in version control or documented publicly. The safety benefit of transparent policies (auditability, community review) must be balanced against the attacker's ability to study them.
When multiple AI agents interact, game-theoretic models help analyze cooperation and defection. Will agents follow safety protocols when it is costly? Under what conditions might an agent find it "rational" to bypass a safety check? Mechanism design from game theory provides tools for designing incentive structures that make compliance the optimal strategy.
Game-theoretic analysis is most useful during threat modeling. When designing a safety system, ask: if an attacker knew exactly how this system works, what would they do? Design the system so that the best attack still fails or is detected.
Game theory assumes rational actors. Attackers may not be perfectly rational, and AI agents do not have true strategic reasoning. Use game theory as a modeling tool for worst-case analysis, not as a precise prediction of actual attacker behavior.
Game theory forces you to think like your adversary. That perspective is invaluable for building systems that withstand strategic attacks.
Explore more guides on AI agent safety, prompt injection, and building secure systems.
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