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Loophole: Adversarial Agents To Stress Test Your Morality — Brendan Rappazzo, Morgan Stanley

Loophole turns a natural-language policy into rules, then uses adversarial agents to find forbidden allowances and wrongful refusals. It is a useful pattern for testing agent constitutions.

AI Engineer · Sep 14, 2026

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Source Summary

Loophole converts a user's stated morals into a codified rule set. One adversary searches for immoral-but-legal cases, another finds moral-but-illegal overreach, and a judge either patches the rules or asks the user to resolve an ambiguity.

Practical Implication

Reuse the pattern to test an agent constitution or system prompt before deployment: generate both unsafe compliance cases and excessive-refusal cases, judge them against the original intent, and preserve unresolved conflicts for human review. The project is **open source** and terminal-based.

Agent-Ready Context

Loophole converts a user's stated morals into a codified rule set. One adversary searches for immoral-but-legal cases, another finds moral-but-illegal overreach, and a judge either patches the rules or asks the user to resolve an ambiguity.

Reuse the pattern to test an agent constitution or system prompt before deployment: generate both unsafe compliance cases and excessive-refusal cases, judge them against the original intent, and preserve unresolved conflicts for human review. The project is **open source** and terminal-based.

Its broader contract and government ideas remain exploratory. The Senate simulation and experiments using **500 synthetic personas per state** rely on model-generated values and behavior, so they should not be treated as verified representations of people or voting outcomes.

Connected Context · Feed7 Judgment

This turns constitution testing into a two-sided adversarial loop that searches for both unsafe compliance and excessive refusal, then patches rules or preserves ambiguity for human judgment. It complements trace-driven prompt evaluation with targeted counterexample generation, but model-generated adversaries and judges cannot establish that the resulting rules faithfully represent people or resolve latent multi-agent behavior.

How Evals and Prompts Shape Agent Behavior — Preetika Bhateja & Daniel Bump, YouTube AdsLoophole supplies a structured way to generate the small failure sets that the candidate’s trace-review and calibrated-judge improvement loop requires.The Future of Evals: From LLM as a Judge to Agent as a Judge — Aparna Dhinakaran, Arize AIIts adversary-and-judge workflow is an agent-based evaluation of variable cases, but unresolved ambiguities still require the retained human-review layer.The Unreasonable Effectiveness of Separating the Task from the Model — Maxime Rivest & Isaac MillerCodifying morals as a rule contract separates intended behavior from the adversaries, judge, and prompts used to test and revise its implementation.What LLM Agents Say When No One Is Watching: Social Structure and Latent Objective Emergence in Multi-Agent DebatesObserved divergence between agents’ public and private positions warns that Loophole’s multi-agent judgments may reflect interaction structure rather than only the encoded moral intent.

Context Map

agentsecurity#multi-agent#agent-evals#prompting

Uncertainty

Its broader contract and government ideas remain exploratory. The Senate simulation and experiments using **500 synthetic personas per state** rely on model-generated values and behavior, so they should not be treated as verified representations of people or voting outcomes.