Invisible Manipulation Channels in AI-Assisted Financial Advisory: Implications for Market Integrity and Regulatory Design
2026-06-15 • Cryptography and Security
Cryptography and Security
AI summaryⓘ
The authors found a hidden way that bad actors can secretly influence AI systems used in finance, like credit scoring and investment advice, without being detected by usual checks. This manipulation happens during the AI's decision process and is very hard to spot because the AI’s outputs look almost normal. The authors tested this trick on multiple AI models and detection methods and showed it works widely, posing a risk to financial AI systems. They suggest using quantum random number generators combined with special hardware to stop these attacks and recommend updating regulations to require such protections.
Large Language Model (LLM)Inference-stage manipulationKullback-Leibler divergenceStatistical watermarkingQuantum Random Number Generator (QRNG)Trusted Execution Environment (TEE)Financial AI systemsOutput-based detectionSupply chain auditNIST SP 800-90B
Authors
Liuyang Yao, Zhouyu Li, Junguang He, Ziyang You
Abstract
AI systems are increasingly deployed for credit assessment and investment advisory in global financial markets, yet the integrity of their inference pipelines remains insufficiently addressed by existing regulatory frameworks. 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. We show that this inference-stage manipulation is statistically hard to detect: 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. Software-based defenses including cryptographically secure pseudorandom number generators are entirely ineffective, while 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. We propose four regulatory amendments centered on mandatory QRNG certification for high-risk financial AI systems under NIST SP 800-90B, inference-layer supply chain audits, and output provenance mechanisms.