Invisible Manipulation Channels in AI Financial Advisors: New Challenges for Market Integrity and Regulatory Design

This article reveals the existence of invisible manipulation channels in the inference sampling layer of large language models (LLMs). Attackers can systematically bias AI-generated financial opinions while maintaining output audit compliance (including statistical watermarks), posing systemic risks to the security of financial market infrastructure. The study verifies that a hardware-level solution combining Quantum Random Number Generator (QRNG) and Trusted Execution Environment (TEE) can block attacks 100% and proposes four regulatory amendments for high-risk financial AI systems.

As AI systems become increasingly prevalent in credit assessment and investment advisory services in global financial markets, the issue of the integrity of their inference pipelines has gradually come to light. Although existing regulatory frameworks regulate the outputs of AI systems, they pay little attention to potential vulnerabilities in the inference process. This regulatory blind spot provides an opportunity for malicious actors, which may seriously affect the integrity of financial markets.

This study identifies and empirically validates an invisible manipulation channel in the LLM inference sampling layer. This vulnerability allows attackers to systematically bias AI-generated financial opinions while fully complying with output-based audit mechanisms (including statistical watermarks).

The study shows that this manipulation during the inference phase is statistically extremely difficult to detect: the Kullback-Leibler divergence between the manipulated output distribution and the normal output distribution can be controlled to be arbitrarily small, meaning any output-based detection scheme requires an unrealistically large sample size to achieve reliable detection, making it a systemic risk to financial infrastructure.

The research team conducted extensive experiments in credit rating and investment advisory scenarios, and the results show:

• Under stealthy manipulation conditions, directional bias keywords can be amplified by 1.8-1.9 times
• Six black-box detectors were successfully bypassed (zero triggers)
• Watermark integrity was fully preserved
• The vulnerability was validated across three mainstream watermark schemes and three heterogeneous model architectures

These results establish the vulnerability as a systemic financial infrastructure risk, with an impact scope far beyond a single model or platform.

Limitations of Software Defense

Software defense based on Cryptographically Secure Pseudorandom Number Generators (CSPRNG) was proven completely ineffective; attackers can precompute manipulation targets by predicting hash keys to bypass protection.

Hardware-Level Solutions

The combination of Quantum Random Number Generator (QRNG) and Trusted Execution Environment (TEE) hardware isolation was proven to achieve 100% attack blocking: replacing predictable hash keys with quantum-derived entropy invalidates all precomputed manipulation targets, reducing the attack success rate to the natural baseline level.

Based on the research findings, the authors propose four regulatory amendments for high-risk financial AI systems:

1. Mandatory QRNG Certification: Require the use of quantum random number generators compliant with the NIST SP 800-90B standard to ensure the unpredictability of the inference process.
2. Inference Layer Supply Chain Audit: Establish an audit mechanism for the supply chain of the AI system's inference layer to ensure the complete chain from deployment to execution complies with security standards.
3. Output Provenance Mechanism: Implement an output provenance mechanism to enable each AI-generated financial opinion to be traced back to a complete record of the inference process.
4. Layered Risk Assessment Framework: Establish a layered regulatory framework based on the risk level of application scenarios, implementing differentiated security requirements for AI financial applications of different risk levels.

Industry Implications

This study reveals key security blind spots in current AI financial applications. As financial institutions rely on AI for decision support, inference layer security will become a core issue in regulation and compliance. Although the QRNG+TEE solution increases deployment costs, it is necessary to maintain market integrity and protect investor interests.

Future Research Directions

• Develop more lightweight randomness enhancement schemes to reduce deployment barriers
• Establish real-time monitoring mechanisms to detect abnormal inference behavior
• Explore security protection in distributed architectures such as federated learning
• Study similar vulnerabilities in multimodal financial AI systems