As financial markets autonomize, new vectors for instability emerge. This whitepaper analyzes the vulnerability of LQMs to data poisoning and the systemic risk of algorithmic herding.
Malicious actors can inject subtle noise into training datasets (e.g., manipulated order books), causing LQMs to learn flawed correlations that manifest as catastrophic trading errors during volatility.
If multiple institutions deploy similar LQMs optimized for the same reward functions, they may simultaneously execute identical trades, amplifying market crashes (Flash Crashes).
Attackers can query an LQM repeatedly to reverse-engineer its proprietary trading logic, allowing them to front-run the model's trades.
Our research demonstrates how a mere 0.05% injection of adversarial data into a training set can deviate an LQM's economic forecast by over 15%. This chart visualizes the divergence between a secure model and a poisoned one during a simulated market event.
"In high-frequency environments, this deviation triggers automated sell-offs before human intervention is possible."
FinanceGPT Labs recommends a multi-layered security framework for Agentic Finance.
Pre-training models against known poisoning attacks to build immunity.
Algorithmic filtering of outliers and anomalies in ingestion pipelines.
Deploying heterogeneous agent ensembles to prevent systemic herding.
Hard-coded logical guardrails that halt execution if variance exceeds safety thresholds.
Deploy agents built on FinanceGPT's secure, adversarial-tested infrastructure.