Adversarial Negotiation Dynamics in Generative Language Models

Language models used in contract negotiations can be exploited through adversarial competition: When different AI systems negotiate against each other, they create opportunities for vulnerabilities to be exposed—including biased, harmful, or legally problematic outputs—suggesting that real-world deployment of these models in high-stakes legal contexts requires careful safety consideration.

The identity and capabilities of opposing AI systems create unknown security risks: Since competing parties may deploy different or unknown language models, organizations cannot fully predict or prepare for how their AI counterpart will behave, highlighting a critical gap in AI safety protocols for competitive business applications.

Current language models lack sufficient "competitive robustness": The paper's evaluation of major open-source models in head-to-head contract negotiations revealed that their safety and performance in adversarial settings remain poorly understood, indicating these systems may not be ready for deployment in genuinely competitive scenarios without additional safeguards.

Adversarial testing through competitive scenarios can improve AI safety: By deliberately placing language models in competitive situations against each other, researchers can identify vulnerabilities and risks that might otherwise go undetected, providing a practical method for red-teaming and improving model reliability before real-world use.

Better model selection and risk mitigation strategies are needed for legal AI applications: The research provides actionable insights for organizations considering AI tools for contract work, emphasizing the importance of understanding model vulnerabilities and implementing safeguards specific to competitive legal contexts.