Verifying Explanation Faithfulness in Hybrid LLM-Rule Financial Decision Agents: A Cryptographically Anchored Audit Framework

A cryptographically anchored audit framework that verifies the faithfulness of explanations in hybrid LLM-rule financial decision agents.

A cryptographically anchored audit framework that verifies the faithfulness of explanations in hybrid LLM-rule financial decision agents. This reference project walks through the whole job, from the problem and the design to the build and the evaluation, so that a developer can study how it works, run it and extend it.

It suits learners who want practical security experience: understanding the threat, building the defence or detector, testing it against realistic attacks and reporting the results.

The main parts are cooperating agents with defined roles and tools, a realistic threat model, the detection or protection mechanism itself, testing against sample attacks, and reporting and alerting of findings. The reference build uses LLM APIs, Python, scikit-learn, Wireshark, and OpenSSL, with a clear project structure, setup instructions and sample data so that it can be run and extended.