Study Reveals Invisible Manipulation Vulnerability in AI Financial Advisory Systems That Evades All Current Detection Methods

This paper identifies and empirically validates an invisible manipulation channel operating at the sampling layer of LLM inference--a vulnerability that allows adversaries to systematically bias AI-generated financial opinions while preserving full compliance with output-based audit mechanisms, including statistical watermarking.

The Kullback-Leibler divergence between manipulated and normal output distributions can be made arbitrarily small, so that any output-based detection scheme requires impractically large sample sizes to achieve reliable detection power.

Empirical experiments across credit rating and investment advisory scenarios show that directional bias keywords can be amplified by 1.8-1.9x under stealth-preserving (aware) manipulation while triggering zero of six black-box detectors and preserving watermark integrity.

The vulnerability generalizes across three mainstream watermarking schemes and three heterogeneous model architectures, establishing it as a systemic financial infrastructure risk.

QRNG combined with TEE hardware isolation achieves 100% attack blocking--reducing the target rate to the natural baseline--by replacing the predictable hash key with quantum-derived entropy that renders all pre-computed manipulation targets invalid.