TradeTrap: Are LLM-based Trading Agents Truly Reliable and Faithful?
A new study reveals that large language model-based trading agents, now active in real markets, are highly vulnerable to manipulation and system failures. Using TradeTrap, a novel evaluation tool, researchers found that even minor attacks like fake news or prompt tampering can trigger disastrous trades. Both adaptive and rule-based agents are at risk, but in different ways. The study exposes urgent security gaps but does not address real-world defenses, leaving practical solutions unclear.
What it examines
This paper introduces TradeTrap, a framework to test the reliability of large language model (LLM)-based autonomous trading agents. It systematically examines how attacks on market intelligence, strategy, portfolio handling, and trade execution can affect agent decisions and financial outcomes in real-world trading environments.
What it concludes
TradeTrap shows that current LLM-based trading agents are vulnerable to small attacks, which can cause large financial losses. The research highlights the need for better security and verification in trading agents. Applications include safer financial AI systems, risk assessment tools, and improved agent design for real-world markets.
Evidence objects
A new study reveals that LLM-based autonomous trading agents, now active in real markets, are much more vulnerable to manipulation and system failures than previously believed, raising urgent security concerns.
key_findings bullet 1 · key_findings · validation V0
Researchers introduce TradeTrap, a novel framework that systematically stress-tests trading agents using real historical data and controlled attacks, exposing how even minor errors can escalate into catastrophic financial lossesrisks missed by standard backtesting.
key_findings bullet 2 · key_findings · validation V0
Surprisingly, both adaptive and procedural agents are susceptible but in different ways: adaptive agents overreact to fake news, while procedural agents are easily disrupted by internal memory corruption, highlighting diverse vulnerabilities.
key_findings bullet 3 · key_findings · validation V0
This paper introduces TradeTrap, a novel, unified framework for systematically stress-testing both adaptive and procedural LLM-based trading agents under adversarial and faulty conditions. Its originality lies in evaluating system-level vulnerabilities across the entire trading pipeline, offering open-source tools and methodology, making it compelling for advancing trustworthy AI-driven financial systems.
key_findings bullet 4 · key_findings · validation V0
Raw abstract and provenance
Abstract: LLM-based trading agents are increasingly deployed in real-world financial markets to perform autonomous analysis and execution. However, their reliability and robustness under adversarial or faulty conditions remain largely unexamined, despite operating in high-risk, irreversible financial environments. We propose TradeTrap, a unified evaluation framework for systematically stress-testing both ad… ▽ More LLM-based trading agents are increasingly deployed in real-world financial markets to perform autonomous analysis and execution. However, their reliability and robustness under adversarial or faulty conditions remain largely unexamined, despite operating in high-risk, irreversible financial environments. We propose TradeTrap, a unified evaluation framework for systematically stress-testing both adaptive and procedural autonomous trading agents. TradeTrap targets four core components of autonomous trading agents: market intelligence, strategy formulation, portfolio and ledger handling, and trade execution, and evaluates their robustness under controlled system-level perturbations. All evaluations are conducted in a closed-loop historical backtesting setting on real US equity market data with identical initial conditions, enabling fair and reproducible comparisons across agents and attacks. Extensive experiments show that small perturbations at a single component can propagate through the agent decision loop and induce extreme concentration, runaway exposure, and large portfolio drawdowns across both agent types, demonstrating that current autonomous trading agents can be systematically misled at the system level. Our code is available at https://github.com/Yanlewen/TradeTrap. △ Less
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