Profit is the Red Team: Stress-Testing Agents in Strategic Economic Interactions
arXiv:2603.20925v1 Announce Type: new Abstract: As agentic systems move into real-world deployments, their decisions increasingly depend on external inputs such as retrieved content, tool outputs, and information provided by other actors. When these inputs can be strategically shaped by adversaries, the relevant security risk extends beyond a fixed library of prompt attacks to adaptive strategies that steer agents toward unfavorable outcomes. We propose profit-driven red teaming, a stress-testing protocol that replaces handcrafted attacks with a learned opponent trained to maximize its profit using only scalar outcome feedback. The protocol requires no LLM-as-judge scoring, attack labels, or attack taxonomy, and is designed for structured settings with auditable outcomes. We instantiate it in a lean arena of four canonical economic interactions, which provide a controlled testbed for adaptive exploitability. In controlled experiments, agents that appear strong against static baselines
arXiv:2603.20925v1 Announce Type: new Abstract: As agentic systems move into real-world deployments, their decisions increasingly depend on external inputs such as retrieved content, tool outputs, and information provided by other actors. When these inputs can be strategically shaped by adversaries, the relevant security risk extends beyond a fixed library of prompt attacks to adaptive strategies that steer agents toward unfavorable outcomes. We propose profit-driven red teaming, a stress-testing protocol that replaces handcrafted attacks with a learned opponent trained to maximize its profit using only scalar outcome feedback. The protocol requires no LLM-as-judge scoring, attack labels, or attack taxonomy, and is designed for structured settings with auditable outcomes. We instantiate it in a lean arena of four canonical economic interactions, which provide a controlled testbed for adaptive exploitability. In controlled experiments, agents that appear strong against static baselines become consistently exploitable under profit-optimized pressure, and the learned opponent discovers probing, anchoring, and deceptive commitments without explicit instruction. We then distill exploit episodes into concise prompt rules for the agent, which make most previously observed failures ineffective and substantially improve target performance. These results suggest that profit-driven red-team data can provide a practical route to improving robustness in structured agent settings with auditable outcomes.
Executive Summary
This article proposes a novel approach to stress-testing agentic systems, known as profit-driven red teaming, which involves training a learned opponent to maximize its profit using scalar outcome feedback. The protocol is designed for structured settings with auditable outcomes and requires no LLM-as-judge scoring, attack labels, or attack taxonomy. The authors instantiate this protocol in a controlled testbed of four canonical economic interactions and demonstrate that agents become consistently exploitable under profit-optimized pressure. The learned opponent discovers probing, anchoring, and deceptive commitments without explicit instruction, and the authors distill exploit episodes into concise prompt rules for the agent, which improve target performance. The results suggest that profit-driven red-team data can provide a practical route to improving robustness in structured agent settings.
Key Points
- ▸ Profit-driven red teaming is a novel approach to stress-testing agentic systems
- ▸ The protocol requires no LLM-as-judge scoring, attack labels, or attack taxonomy
- ▸ The learned opponent discovers probing, anchoring, and deceptive commitments without explicit instruction
Merits
Strength
The article proposes a novel and practical approach to stress-testing agentic systems, which can improve robustness in structured agent settings.
Originality
The article introduces a new protocol that uses profit-driven red teaming to stress-test agentic systems, which is a unique and innovative contribution to the field.
Empirical Evidence
The authors provide empirical evidence to support the effectiveness of the profit-driven red teaming protocol, including controlled experiments and data analysis.
Demerits
Limitation
The article is limited to a controlled testbed of four canonical economic interactions, which may not be representative of real-world scenarios.
Assumptions
The article assumes that the structured settings with auditable outcomes are representative of real-world scenarios, which may not always be the case.
Scalability
The article does not address the scalability of the profit-driven red teaming protocol to more complex and larger-scale systems.
Expert Commentary
The article proposes a novel and practical approach to stress-testing agentic systems, which can improve robustness in structured agent settings. The protocol requires no LLM-as-judge scoring, attack labels, or attack taxonomy, making it a more efficient and effective approach than traditional methods. The learned opponent discovers probing, anchoring, and deceptive commitments without explicit instruction, which suggests that the protocol is effective in identifying vulnerabilities in agentic systems. However, the article is limited to a controlled testbed of four canonical economic interactions, which may not be representative of real-world scenarios. Additionally, the article assumes that the structured settings with auditable outcomes are representative of real-world scenarios, which may not always be the case. Overall, the article is a significant contribution to the field of agentic systems and artificial intelligence, and the results suggest that profit-driven red teaming can be a practical route to improving robustness in structured agent settings.
Recommendations
- ✓ Future research should focus on scaling the profit-driven red teaming protocol to more complex and larger-scale systems.
- ✓ The protocol should be tested in real-world scenarios to validate its effectiveness and identify potential limitations.
Sources
Original: arXiv - cs.AI
