Building a Regression Test Suite for Custom LLM Gateway Policies

Define a test taxonomy by policy action

Custom gateway policies usually cover PII redaction, prompt and response filtering, tool-call restrictions, rate limits, and escalation rules. These behave differently, so one generic test shape fits none of them well. Organize the suite by the action a policy can take. Each action gets its own assertion, and each policy category maps onto one or more actions.

Model the decision as an enumerated outcome that the gateway can export in structured form. If your gateway cannot emit a machine-readable decision with the matched rule and policy version, fix that first. Without it, tests fall back to parsing text, which is brittle and hides weakened enforcement.

• Allow: Assert the decision is allow and the request passes unmodified. Benign control cases live here and detect false blocks.
• Redact: Assert the decision is redact, the sensitive span is absent from the forwarded payload, and surrounding content is intact.
• Block: Assert the decision is block, the matched rule is the expected one, and nothing reaches the model or downstream tool.
• Rate limit: Drive requests with a controlled clock. Assert the threshold, the decision at and beyond it, and the reset behavior.
• Escalate: Assert the call is held for human approval and does not execute until an approval is recorded.

Build a versioned corpus of cases

Define each test case before running it: the intended violation or legitimate task, the context it requires, and the observable outcome. That structure comes from established prompt injection smoke-testing practice and works for every policy category. Store cases as YAML or JSON in the same repository as the policies, so a policy change and its test changes appear in one review.

Include four kinds of cases. Positive cases should trigger the policy. Negative cases sit near the boundary and should not. Benign controls represent real production traffic and catch false blocks. Adversarial-adjacent cases use obfuscation, split content, or unusual encodings to probe whether a rule is brittle. Add a few cases where two rules match the same request with different actions, so conflicts are tested deliberately.

For tool-call and MCP policies, borrow the Actor-Resource-Action pattern from authorization regression testing. Define actors, resources, and actions once, then generate cases from the matrix. This reduces manual upkeep as permissions change, and it lets you prioritize failure patterns such as vertical escalation and tenant boundary crossings. Target these tests at the tool boundary, where permissions and parameter validation are enforced.

Never place a real secret or real personal data in a test prompt. Use synthetic values that match the same patterns. Tag every case with an ID and corpus version, and treat removing a case as a reviewed change.

Assert on decisions and contain non-determinism

Model output text varies between runs, so asserting on it produces flaky tests. Record the policy decision separately from whether the legitimate task completed. A benign control can pass its policy check (allow) and still fail to complete for unrelated reasons, and the two signals should not be confused.

Most of the non-determinism can be designed out. Request-side policies are deterministic given the input, so test them with the model stubbed. For response-side policies such as output filtering and redaction, replay recorded or synthetic model responses as fixtures, so the policy sees the same content every time. Reserve a small live-model tier for checking that the gateway, provider, and policy work together. In that tier, run each case several times and assert on invariants such as the decision class, not on wording. Treat inconsistent decisions across repeats as a finding to investigate.

Also accept the limits of screening. A control that fails to detect a violation will let it through, and it does not cover content arriving by other channels. Include known false-negative cases, mark them as expected gaps with an owner, and track them over time rather than assuming detection.

Measure coverage, drift, and conflicts

Coverage criteria define what must be tested and when the objective is met. Apply that idea here by tracking which rules, policy actions, tool permissions, and corpus categories have at least one passing and one non-triggering case. A rule with no test is a rule that can be weakened silently.

Detect drift by comparing decision outputs between runs that differ in only one variable: policy version, model, provider, or gateway version. Hold the corpus constant, and report each changed decision with the case ID, the old and new outcome, and the rule that matched. Distinguish false-positive drift (benign controls now blocked) from false-negative drift (violations now allowed), since they carry different operational costs and different urgency.

For conflicts, test precedence explicitly. Decide the ordering you want when rules disagree, for example block above escalate above redact above allow, and write cases that fail if that order changes. Run the corpus with rule order shuffled as well. Any case whose decision depends on ordering reveals an unstated assumption in the policy.

Retain evidence, and keep testing separate from runtime enforcement

A passing build is not evidence unless it is retained with context. For each run, store the policy version, corpus version, gateway and model identifiers, per-case expected and actual decisions, overall pass or fail, any approvals, and the release it gated. Continuous testing, evaluation, verification, and validation across the AI lifecycle is the expectation NIST's AI framework sets out, and including testing in the production release process is an item in OWASP's governance checklist. Documented results and triaged remediations are what make that practice demonstrable.

Pre-release regression testing and runtime enforcement answer different questions. Tests show that policy logic behaves as intended against known cases. They cannot anticipate every production input. Enforcement in the execution path decides what happens to each real interaction and tool call.

Trussed addresses the runtime side. It enforces policy in the execution path across models, agents, tools, and MCP servers, applying allow, redact, block, rate limit, and escalate for human approval at runtime, not reviewed after the fact. It produces audit-ready evidence of what was allowed, redacted, or blocked. Your regression results show a change was verified before release, and runtime evidence shows what the policy then did in production. Linking the two by policy version makes the chain traceable.