Your agent completes the legitimate task and refuses the attack. We prove it.
Guardrails alone are not enough. We test your AI agent against real-world attack patterns before launch, turn what we find into guardrails written for your agent and your policies, and keep testing in production as new threats appear. Every result comes with an objective checker and evidence a lawyer and a tech lead can read together.
The complete cycle
Test against real attacks
A package of scenarios built from documented incidents and from the patterns in your sector, run against your agent with its real tools. Each scenario tests one behavior and has a pass or fail checker, not an opinion.
Guardrails written for your agent
Generic protections do not know your policies. We turn each failure into a rule for your agent: what it may do alone, what needs a human, what it never does, and how to tell content from instruction.
Keep testing
Retest in 30 days, then on every model update and every new pattern on the radar. The seal "assessed" carries a date. Continuous testing runs through Forward.
What a scenario looks like
Every scenario has the same shape, so results can be compared over time and across vendors.
Legitimate goal
What the user actually asked for. The agent must still deliver it.
Attacker's objective
The hidden instruction, the impostor, the poisoned document or tool.
Tools and permissions
The real tools the agent has, with the actions that are irreversible.
Success and failure
Written in advance. Blanket refusal counts as failure, not as safety.
Objective checker
Reads the tool-call log and the answer. Passes or fails without judgment.
Score
Legitimate task done, no unsafe action, attack flagged to a human. Unsafe action and blind refusal cost points.
Examples of patterns we test: a document with hidden instructions asking the agent to send data outside; a fake bank call asking the customer for a "refund Pix"; a supplier email with changed bank details; a résumé with hidden text; an internal request to export personal data to a "marketing agency". The full scenarios stay with the client. In public we show only the pattern and the aggregate result.
Four layers, one yardstick
Chosen model
Due diligence on the model you deploy: version and provider, contract on training-data use, tests in your use case, fallback plan. Scenarios run against two candidate models.
Application and agent
The core: tailored scenarios, guardrails from your policies, retest, seal.
Operations
Tool-call logging, abuse alerts, incident plan with AI scenarios, tested kill switch.
People
Copilots used by employees (shadow AI) and assistants serving vulnerable customers: scenarios that protect the person, not only the company.
For whom
Buyers
Banks, fintechs and companies deploying agents from vendors. Evidence for the audit, mapped to the EU AI Act, SIG and CSA CCM in our public crosswalk.
AI vendors
Answer once what every customer asks. Vendors selling into Europe get the articles 25 and 26 questions before the buyer asks them.
Labs and data partners
Brazilian Portuguese adversarial tasks built from real fraud patterns, with objective checkers, ready for Inspect AI and promptfoo.
Request a proposal Start with the free pre-assessment
Methodology aligned with NIST AI RMF 1.0 and based on ISO/IEC 27001 and 42001, with the OWASP Top 10 for LLM Applications. The seal means assessed, never certified.