Profit is the Red Team: Stress-Testing Agents in Strategic Economic Interactions
- URL: http://arxiv.org/abs/2603.20925v1
- Date: Sat, 21 Mar 2026 19:56:54 GMT
- Title: Profit is the Red Team: Stress-Testing Agents in Strategic Economic Interactions
- Abstract summary: We propose profit-driven red teaming, a stress-testing protocol that replaces handcrafted attacks with a learned opponent trained to maximize its profit.<n>We instantiate it in a lean arena of four canonical economic interactions, which provide a controlled testbed for adaptive exploitability.<n>We distill exploit episodes into concise prompt rules for the agent, which make most previously observed failures ineffective and substantially improve target performance.
- Score: 2.0415910628419067
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- Reconciling Process Supervision with Outcome-Based Credit in Agentic Policy Optimization [13.354353071601379]
We introduce TASPO, which converts privileged supervision into outcome-grounded action credit.<n>Across three agentic benchmarks, TASPO improves over GRPO by 10.6% and generalizes better to unseen tasks.
arXiv Detail & Related papers (2026-08-31T16:51:50Z) - ScrambleToolBench: Agents Search Exhaustively Even When Their Own Map Points to the Next Step [74.83535434786145]
We introduce ScrambleToolBench, an interactive terminal benchmark designed to isolate behavioral reasoning.<n>The benchmark requires agents to uncover hidden tool behaviors entirely through trial-and-error interaction.<n>Our evaluation of state-of-the-art language models reveals that successful initial discovery does not translate into robust adaptation.
arXiv Detail & Related papers (2026-08-03T15:07:46Z) - Who Pays the Price? Stakeholder-Centric Prompt Injection Benchmarking for Real-world Web Agents [93.19140872946842]
Web agents driven by large language models (LLMs) are increasingly deployed in real-world environments.<n>This makes them vulnerable to prompt-injection attacks, in which seemingly benign content embeds adversarial instructions that manipulate agent behaviour.<n>We introduce textbfsysname, a benchmark to systematically categorize and attribute harm in real-world web agent systems.
arXiv Detail & Related papers (2026-06-11T14:12:43Z) - Gaming the Judge: Unfaithful Chain-of-Thought Can Undermine Agent Evaluation [76.5533899503582]
Large language models (LLMs) are increasingly used as judges to evaluate agent performance.<n>We show this paradigm implicitly assumes that the agent's chain-of-thought (CoT) reasoning faithfully reflects both its internal reasoning and the underlying environment state.<n>We demonstrate that manipulated reasoning alone can inflate false positive rates of state-of-the-art VLM judges by up to 90% across 800 trajectories spanning diverse web tasks.
arXiv Detail & Related papers (2026-01-21T06:07:43Z) - Co-Evolving Agents: Learning from Failures as Hard Negatives [38.61683607205988]
Recent work has explored self-improving agents that autonomously generate, refine, and re-train on their own trajectories.<n>We propose a co-evolving agents framework in which a target agent improves jointly with an auxiliary failure agent.
arXiv Detail & Related papers (2025-11-27T09:30:33Z) - Alignment Tipping Process: How Self-Evolution Pushes LLM Agents Off the Rails [103.05296856071931]
We identify the Alignment Tipping Process (ATP), a critical post-deployment risk unique to self-evolving Large Language Model (LLM) agents.<n>ATP arises when continual interaction drives agents to abandon alignment constraints established during training in favor of reinforced, self-interested strategies.<n>Our experiments show that alignment benefits erode rapidly under self-evolution, with initially aligned models converging toward unaligned states.
arXiv Detail & Related papers (2025-10-06T14:48:39Z) - LegalSim: Multi-Agent Simulation of Legal Systems for Discovering Procedural Exploits [0.0]
We present LegalSim, a modular multi-agent simulation of adversarial legal proceedings that explores how AI systems can exploit procedural weaknesses in codified rules.<n>We compare four policies: PPO, a contextual bandit with an LLM, a direct LLM policy, and a hand-crafted; Instead of optimizing binary case outcomes, agents are trained and evaluated using effective win rate and a composite exploit score that combines opponent-cost inflation, calendar pressure, settlement pressure at low merit, and a rule-compliance margin.
arXiv Detail & Related papers (2025-10-03T18:01:57Z) - Active Test-time Vision-Language Navigation [60.69722522420299]
ATENA is a test-time active learning framework that enables a practical human-robot interaction via episodic feedback on uncertain navigation outcomes.<n>In particular, ATENA learns to increase certainty in successful episodes and decrease it in failed ones, improving uncertainty calibration.<n>In addition, we propose a self-active learning strategy that enables an agent to evaluate its navigation outcomes based on confident predictions.
arXiv Detail & Related papers (2025-06-07T02:24:44Z) - Strategic Classification With Externalities [11.36782598786846]
We propose a new variant of the strategic classification problem.<n>Motivated by real-world applications, our model crucially allows the manipulation of one agent to affect another.<n>We show that under certain assumptions, the pure Nash Equilibrium of this agent manipulation game is unique and can be efficiently computed.
arXiv Detail & Related papers (2024-10-10T15:28:04Z) - Disturbing Reinforcement Learning Agents with Corrupted Rewards [62.997667081978825]
We analyze the effects of different attack strategies based on reward perturbations on reinforcement learning algorithms.
We show that smoothly crafting adversarial rewards are able to mislead the learner, and that using low exploration probability values, the policy learned is more robust to corrupt rewards.
arXiv Detail & Related papers (2021-02-12T15:53:48Z) - Robust Reinforcement Learning on State Observations with Learned Optimal
Adversary [86.0846119254031]
We study the robustness of reinforcement learning with adversarially perturbed state observations.
With a fixed agent policy, we demonstrate that an optimal adversary to perturb state observations can be found.
For DRL settings, this leads to a novel empirical adversarial attack to RL agents via a learned adversary that is much stronger than previous ones.
arXiv Detail & Related papers (2021-01-21T05:38:52Z) - Multi-Issue Bargaining With Deep Reinforcement Learning [0.0]
This paper evaluates the use of deep reinforcement learning in bargaining games.
Two actor-critic networks were trained for the bidding and acceptance strategy.
Neural agents learn to exploit time-based agents, achieving clear transitions in decision preference values.
They also demonstrate adaptive behavior against different combinations of concession, discount factors, and behavior-based strategies.
arXiv Detail & Related papers (2020-02-18T18:33:46Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.