Logical language model prompts for fraud detection

Abstract

In some embodiments, a security chatbot comprises a generative language module (GLM) and a prompt manager configured to dynamically update the GLM input prompt according to the output of the GLM. The input prompt instructs the GLM to carry out a task (e.g., determine whether a target message received by a user is indicative of online fraud) and in response, to output a specific flag token. In response to detecting the flag token within the output of the GLM, the prompt manager may selectively execute a code snippet identified according to the respective flag token. Executing the code snippet effectively updates the prompt, which is then fed back to the GLM for the next inference step. In contrast to conventional LLM prompting, updates to the prompt herein comprise modifications (e.g., insertion of other flag tokens, etc.) other than merely appending an inferred token to the previous prompt. Furthermore, the prompt modifications are performed inline, i.e., without submitting an entirely new prompt to the GLM.