The Token Economics of AI AppSec Agents

In a study conducted by Endor Labs, the effect of using precomputed, deterministic evidence versus deriving security facts independently in AI Application Security (AppSec) deployments was assessed. The research involved a controlled benchmark using the same model and 34 AppSec prompts across 12 large open-source projects, comparing two agent configurations: one with access to precomputed evidence and the other relying on local repositories and public web research. The evidence-equipped agent demonstrated significant efficiency, requiring 91.7% fewer tokens, completing tasks 2.8 times faster, and making 77.6% fewer tool calls compared to the unequipped agent. The findings revealed that the absence of precomputed evidence leads to higher costs, especially as codebase size increases, and that agents tasked with generating facts independently tend to be less reliable and more costly. The study highlights the importance of providing agents with precomputed facts to enhance their efficiency and accuracy in synthesizing security analyses.

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