Impartial — agentic threat model

Not certain from the listing — the underlying foundation models are not specified. Threats include adversarial prompt injection by users trying to bias the AI lawyer or judge, and model misalignment leading to offensive or biased verdicts.

Not certain from the listing — the data pipeline for storing and processing user arguments is not detailed. Threats include data leakage of sensitive or private arguments submitted during 'blind inputs' and potential data poisoning if past debates are used for fine-tuning.

Not certain from the listing — the orchestration framework managing the AI lawyers and AI judge is undisclosed. Threats include insecure state handling during the multi-step argument refinement and evaluation process.

Not certain from the listing — hosting and sandboxing details are omitted. Threats include standard web application vulnerabilities, lack of isolation between concurrent user debate sessions, and potential denial of service.

Not certain from the listing — no monitoring or guardrail systems are mentioned. Threats include evaluation gaming where users craft inputs to exploit the judge's scoring system, and a lack of drift detection for the judging criteria.

Not certain from the listing — compliance frameworks, user authentication, and data retention policies are not described. Threats include unauthorized access to private debate sessions and lack of audit trails for the AI's decision-making process.

The agent explicitly operates as a multi-agent system where 'AI lawyers' refine arguments and present them to an 'AI judge'. Threats include agent-to-agent trust abuse, where a compromised lawyer agent could inject malicious payloads or manipulate the judge agent's evaluation logic.