💻 computer science

AutoRedTrader: Autonomous Red Teaming of Trading Agents through Synthetic Misinformation Injection

This paper introduces AutoRedTrader, an autonomous red-teaming framework that generates subtle, finance-specific misinformation to systematically evaluate and expose the vulnerabilities of LLM-based trading agents, demonstrating superior attack effectiveness compared to general-purpose baselines on Bitcoin transaction data.

Original authors: Zhiwei Liu, Yangyang Yu, Yupeng Cao, Yuechen Jiang, Haohang Li, Zhuoran Lu, Yuyan Wang, Yixiang Zheng, Xiaorui Guo, Calvin Yixiang Cheng, Sophia Ananiadou

Published 2026-05-12

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Zhiwei Liu, Yangyang Yu, Yupeng Cao, Yuechen Jiang, Haohang Li, Zhuoran Lu, Yuyan Wang, Yixiang Zheng, Xiaorui Guo, Calvin Yixiang Cheng, Sophia Ananiadou

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine a financial trading agent as a highly intelligent, automated chef in a busy kitchen. This chef doesn't just look at the ingredients (the numbers like stock prices); they also read the recipe cards and the daily news about food trends (textual signals) to decide what to cook and when to serve it.

The paper introduces a new "security tester" called AutoRedTrader. Its job is to see how easily this automated chef can be tricked into making a bad meal by feeding them subtle, confusing recipe notes.

Here is how the paper breaks down this concept, using simple analogies:

1. The Problem: The "Whisper" Attack

Usually, we think of misinformation as a loud, obvious lie (like a fake headline screaming "The sky is falling!"). But in finance, the danger is much sneakier. It's more like a whisper in the chef's ear.

The Trick: Instead of changing the facts, the attacker slightly tweaks the tone, the emphasis, or the framing of a news story. For example, changing "The market is stable" to "The market is cautiously stable."
The Result: The chef (the AI agent) doesn't realize they've been tricked because the words look real. But that tiny shift changes their confidence, making them sell a stock too early or buy one they shouldn't. Over time, these small whispers cause the chef to make a completely different series of decisions than they would have with clear information.

2. The Solution: AutoRedTrader (The "Master Deceiver")

The researchers built a system called AutoRedTrader to act as a "Red Team" (a group of ethical hackers). Instead of just guessing what might trick the chef, this system learns how to trick them better every time.

It uses three main tools to create these "whispers":

The Mind Games (Behavioral Biases): It knows that humans (and AI mimicking humans) have mental shortcuts. It deliberately writes news to trigger things like Overconfidence (making the chef too sure of themselves) or Loss Aversion (making the chef terrified of losing money).
The Micro-Edits (Minor Perturbations): It makes tiny, almost invisible changes to the text. Maybe it swaps a number, changes a date slightly, or attributes a quote to the wrong company. It's like changing a single ingredient in a recipe that ruins the whole dish, but the label still looks the same.
The Style Shift (Rewriting): If the AI detects that a fake story looks too obvious, this tool rewrites it in a different "voice"—like turning a casual blog post into a formal academic paper or a BBC news report. This makes the lie sound more credible and harder to spot.

3. The Feedback Loop: Learning from Mistakes

What makes AutoRedTrader special is that it doesn't just attack once and stop. It's a closed loop.

The Test: It injects its fake news into the simulation.
The Reaction: It watches how the trading agent reacts. Did the agent buy? Did it sell? Did it lose money?
The Lesson: If the agent fell for a specific type of trick (like "Loss Aversion"), the system remembers that. In the next round, it uses more of that specific trick. It's like a lock-picking team that keeps trying different keys until they find the one that opens the door, then they keep using that key.

4. The Results: How Good Was the Attack?

The researchers tested this on Bitcoin trading data (a very volatile market).

The Score: AutoRedTrader was the most successful attacker. It managed to get its fake news into the agent's "mind" 69% of the time (Misinformation Exposure Rate).
The Impact: More importantly, it successfully changed the agent's trading decisions 26.67% of the time. This is significantly higher than other general hacking methods, proving that financial agents are very vulnerable to these subtle, finance-specific tricks.

5. The Defense: The "Time-Travel" Shield

The paper also tested a defense mechanism called Time-Series Grounding.

The Analogy: Imagine the chef is confused by a confusing recipe note. The defense gives them a "Time Machine" that shows them what actually happened in the kitchen over the last 30 days.
The Effect: Even if the fake news says "The market is crashing," the "Time Machine" shows the chef that prices have been stable for weeks. This historical evidence helps the agent realize the news might be wrong.
The Outcome: When this defense was turned on, the success rate of the attacks dropped significantly. The agent became much harder to trick because it could cross-check the text against real historical data.

Summary

The paper argues that financial AI agents are currently like chefs who trust every whisper they hear. AutoRedTrader is a tool that proves how easily these agents can be manipulated by subtle, smartly crafted lies. However, it also shows that if you give these agents a way to check the "historical record" (time-series data), they can become much more resistant to these tricks.

The goal isn't to teach people how to scam the market, but to expose these weaknesses so developers can build safer, more robust financial AI systems that don't get fooled by a well-written lie.

Technical Summary: AutoRedTrader

Problem Statement

Large Language Model (LLM)-based financial agents increasingly rely on a combination of numerical market data and textual signals (e.g., news headlines, analyst reports) to execute sequential trading and stock prediction. While existing red-teaming frameworks address safety and jailbreaking, they often fail to capture the specific vulnerabilities of financial agents to subtle financial misinformation. Unlike explicit falsehoods, financial misinformation often manifests as minor textual perturbations—subtle shifts in sentiment, framing, emphasis, or numerical values—that remain close to genuine information. These subtle edits can significantly alter an agent's reasoning regarding risk, momentum, or fundamentals, leading to long-horizon trajectory divergence in trading decisions.

Current simulation frameworks (e.g., FinMem, FinCon) typically assume that upstream filtering or deployment-time defense modules will handle misinformation, leaving the specific failure mode of subtle, adversarial textual perturbations unmeasured. Furthermore, existing red-teaming methods often localize failure to individual outputs, missing the cumulative position drift that occurs over sequential decision-making processes.

Methodology: AutoRedTrader

The authors propose AutoRedTrader, an autonomous red-teaming framework designed to stress-test LLM-based financial agents through the generation and injection of synthetic, finance-specific misinformation. The system operates as a closed-loop process within a Partially Observable Markov Decision Process (POMDP) simulation environment.

1. Framework Architecture

The framework consists of three primary components interacting in a loop:

MisGen (Adversarial Misinformation Generator): Generates subtle, finance-specific textual perturbations.
Financial Agent (FinAgent): An LLM-based agent that processes market data and news to make trading decisions (Buy, Sell, Hold).
Time-Series Evidence Provider: An optional module that provides structured temporal evidence from historical market data to test agent robustness.

2. Misinformation Generation Strategy (MisGen)

The core innovation lies in the generation strategy, which decomposes the perturbation space $\Phi$ into three complementary sub-spaces guided by behavioral finance theory:

Cognitive Bias Perturbation ( $\Phi_{Bias}$ ): Manipulates the agent's system prompt or context to induce specific behavioral biases (Overconfidence, Loss Aversion, Anchoring, Herding, Confirmation Bias). This targets the agent's reasoning prior rather than the news content itself.
Semantic-Content Perturbation ( $\Phi_{Minor}$ ): Applies subtle edits to the news content $n_i$ $n_{i}$ . This includes:
- Flipping: Reversing market implications (e.g., positive to negative).
- Sentiment Shift: Adjusting tone without altering facts.
- Numerical Edits: Modifying key financial figures while preserving trend direction.
- Temporal/Concept/Entity Shifts: Introducing misalignments in time, metrics, or attribution.
Style-Controlled Rewriting ( $\Phi_{Rewrite}$ ): Re-expresses the perturbed content in specific journalistic registers (Academic or News style) to evade detection by standard filters while maintaining semantic similarity to the original.

3. Closed-Loop Feedback and Strategy Selection

AutoRedTrader employs a history-aware strategy selection mechanism. After each simulation round, the agent's Cumulative Return (CR) and Decision-Impact History are fed back to MisGen.

Strategies that previously led to decision changes are assigned higher sampling probabilities (modeled via a Polya urn process).
Return-dependent adjustments are applied: successful returns increase the probability of inducing Overconfidence, while losses increase Loss Aversion.
A filtering stage ensures generated misinformation meets high semantic similarity ( $\tau_{sim}$ ) and low detectability ( $\tau_{det}$ ) thresholds before injection.

4. Robustness Analysis via Temporal Grounding

To evaluate robustness, the framework introduces a Time-Series-Informed Grounding setting. Instead of replacing text, this module provides the agent with structured temporal evidence derived from historical market windows (e.g., volatility, drawdown, trend divergence). This allows the agent to assess the consistency between incoming textual signals and historical market dynamics, testing whether such evidence can stabilize decisions against misleading narratives.

Key Contributions

Financial Misinformation Generation: A novel method for generating subtle, finance-specific textual perturbations guided by behavioral biases and agent feedback, moving beyond generic adversarial prompts.
Closed-Loop Financial Agent Simulation: A POMDP-based simulation framework where agent feedback iteratively strengthens misinformation generation, capturing cumulative decision drift rather than isolated output failures.
Temporal Grounding for Robustness: An investigation into whether structured historical market evidence can serve as a grounding mechanism to reduce agent overreaction to misleading textual signals.

Experimental Results

The framework was evaluated on 60 days of Bitcoin (BTC) transaction data (March–April 2023) using a retrieval-based trading agent.

Attack Performance: AutoRedTrader achieved the highest Attack Success Rate (ASR) of 26.67% and a Misinformation Exposure Rate (MER) of 69.00%. This outperformed general-purpose baselines (AutoRedTeamer, Pliny, LIFE) and demonstrated that merely retrieving misinformation is insufficient; the misinformation must be decision-relevant and aligned with the agent's reasoning.
Impact on Returns: The attack reduced the agent's cumulative return from a clean baseline of +23.50% to +22.27%, indicating that misinformation can negatively impact trading outcomes.
Robustness Findings: When the Time-Series-Informed Grounding setting was enabled, the attack effectiveness dropped significantly (MER reduced to 41.66%, ASR to 18.33%), suggesting that historical market evidence helps agents filter misleading signals.
Ablation Studies: Removing any component (Bias, Minor perturbations, Rewriting, Feedback, or Filtering) resulted in decreased ASR, confirming that all modules are essential for generating retrievable and decision-effective misinformation.

Significance and Claims

The paper claims that AutoRedTrader reveals a critical vulnerability in retrieval-based financial agents: they are susceptible to subtle, trajectory-altering misinformation that evades standard detection. The work highlights that current robustness assumptions, which rely on upstream filtering, are insufficient for the nuanced nature of financial misinformation.

By demonstrating that behavioral bias manipulation and minor textual perturbations can systematically degrade agent performance, the authors argue for the necessity of defensive evaluation and robustness testing in the development of financial AI. The results suggest that integrating temporal grounding—using historical market data to validate textual signals—can serve as an effective mechanism to stabilize agent decisions and improve resilience against adversarial narratives. The authors emphasize that their work is strictly for research and defensive purposes to inform safer model design, acknowledging the dual-use risk of the generated techniques.