SoK: Trading Agents or Market Crashers? Dissecting Robustness and Security Failures in Academic Financial LLM Trading Schemes
FARSIGHT framework finds 80% of 15 academic financial LLM trading schemes fail robustness metrics and 100% exhibit security vulnerabilities.
The paper introduces FARSIGHT, a scheme-level evaluation framework for financial LLM trading agents covering robustness under market turbulence, including flash-crash-like scenarios, and security against attacks on information sources, on agents, and agent-as-attacker behaviors. Applied to 15 representative academic schemes, 80% fail at least one core robustness metric and all 15 show security vulnerabilities. The authors warn the two failure modes are inseparable since small misjudgments can cascade into market-wide crashes that adversaries can deliberately trigger at minimal cost.
- Evaluated 15 academic financial LLM trading schemes
- 80% fail at least one core robustness metric
- 100% exhibit security vulnerabilities across three attack types
- Compromised agents hold direct execution authority over real capital
From the original article
1 min read at arxiv.orgAutonomous large language model (LLM) agents are moving rapidly into high-stakes domains, yet existing agentic-AI security studies remain largely domain-agnostic and overlook the distinctive, high-consequence attack surface such settings create.
By Mengxiao Wang · arxiv.org
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