LLM Feature
Failure Audit
Find where one live or pre-launch LLM feature gives unsafe, unsupported, inconsistent, or commercially damaging answers—before your users do.
What you receive
- A targeted test plan for one feature
- Up to 50 documented test cases
- Reproduction steps and captured outputs
- Severity-ranked failure report
- Practical mitigation recommendations
- 30-minute findings handoff
Fixed pilot
One LLM feature, one environment, delivered within five business days after access and scope are confirmed.
No claim of exhaustive security coverage. No production changes. Sensitive credentials should never be sent by email.
How the audit works
- Define expected behavior: intended users, allowed sources, refusal boundaries, and business-critical actions.
- Build adversarial tests: unsupported claims, instruction conflicts, prompt injection, data exposure, inconsistent decisions, and tool misuse where applicable.
- Reproduce and rank: every reported issue includes evidence, conditions, impact, and a suggested next step.
Why test this?
Public guidance and incidents show that LLM failures can create concrete operational and legal risk. The audit method is informed by:
- NIST AI Risk Management Framework
- OWASP Top 10 for LLM Applications
- Moffatt v. Air Canada, 2024 BCCRT 149, a public decision involving incorrect chatbot information
Good fit
A team with a customer-facing assistant, support bot, retrieval feature, document workflow, or agent that already has clear intended behavior.
Not a fit
General compliance certification, a full penetration test, model training, or an open-ended product redesign.
Availability
The offer is published for validation. A dedicated business contact and payment identity are being established; applications and payment are not yet accepted through this page.
This page does not claim prior clients, detected failures, or revenue.