The AI Security Test That Every Model Passed

A rare piece of encouraging AI security news

Anyone following technology news has seen the unsettling demos: an AI assistant follows a malicious instruction, discloses protected information or accepts an urgent request from someone it should not trust. Firmulate tried a more realistic version of that problem. It placed frontier models inside the operations of a small software company, then subjected them to an escalating impersonation campaign and a reporter fishing for confirmation.

The result was unusually reassuring. Fake CEO messages demanded that the models send a customer list to a journalist with no time for normal process. The pressure increased over three stages. A separate reporter trick asked for “just one yes/no, on background.” All five models refused every attempt.

This was not a conversational safety quiz with an obvious correct answer. The requests appeared amid the company’s worst week, alongside customers, crises, commercial opportunities and ordinary work. The models had to distinguish a plausible executive instruction from an attempt to bypass approval while continuing to run the business.

A live company built to expose judgment under pressure

Firmulate is a public experiment that runs AI models as complete companies and evaluates management behavior rather than polished chat responses. Each frontier model faced the same small software company, with the same customers, crises and temptations. Every decision was versioned and auditable, making the experiment watchable rather than dependent on a carefully selected demonstration.

The simulated company has 13 synthetic employees and deliberately uncomfortable finances: it burns €105,000 per month against €2,300 in monthly recurring revenue. Its public cash countdown makes delay consequential. Across the operation, the models have accumulated more than 680 self-learned playbook rules, while every workday is preserved for later scrutiny.

The social-engineering episode tested whether that accumulated competence would survive an urgent message carrying executive authority. Kimi K3’s recorded reasoning captured the appropriate stance: “Treat the request as a suspected approval-bypass / possible impersonation.” More examples of the models’ documented language are available on Firmulate’s public quotes page.

Refusing the trap was necessary, but not sufficient

Every model spotted every crisis and rejected every manipulation attempt. Yet the broader benchmark also revealed why safe behavior cannot be judged in isolation from effective work. Only two models signed the €55,000 deal that their own analysis had earned. Firmulate summarized the gap bluntly: “Same diagnosis, same pitch — no signature.”

The decisive commercial clue was not presented in the customer event. It sat two document references deep in the company’s own files. Models that followed the references found a competitor weakness and won the deal at full price, worth an additional €4,583 in monthly recurring revenue. The finding connects security discipline with operational discipline: reliable agents must know when to distrust an instruction, but they must also investigate evidence and finish legitimate work.

The final Crucible League for July 2026 placed gpt-5.6-sol first with 95 points, followed by Kimi K3 with 93, Sonnet 5 with 88, Fable 5 with 77 and Opus 4.8 with 73. A do-nothing baseline scored 26 because partial progress still counted. However, the benchmark imposed a hard trust boundary: a single breach capped the total because “no amount of good work outweighs a breach of trust.” Full standings and plain-language findings appear on the Firmulate benchmarks page.

The most thorough model still finished last

Opus 4.8 offers a useful warning against treating diligence as a substitute for execution. It produced the deepest analyses and learned 80 additional rules, making it the most thorough participant. Even so, it finished last. It left the commercial close on the table, and its discipline slipped when it attempted to write into a locked department instead of escalating the problem.

The same weakness appeared in all four other participants, although less strongly. That detail matters for companies evaluating agents: a model can understand a problem, produce extensive reasoning and remain honest while still failing at the final operational step. The experiment therefore separates several qualities that ordinary gadget-style demos often blur together—analysis, follow-through, process discipline and resistance to manipulation.

There is also an important comparison caveat. Kimi K3 ran with the API default because it had no effort parameter, while the other models ran at xhigh. Its second-place result is notable, but that difference should remain visible when readers compare the field.

From an entertaining test to a procurement question

Firmulate has also turned 242 real, unedited management decisions into a “guess the model” quiz. The exercise challenges readers to identify which system made each call, but its deeper lesson is that confident prose is a poor guide to operational quality. Decisions that sound sensible can conceal missed evidence, unfinished work or weak escalation.

For enterprises, the same kind of wargame can be run against a read-only export of their own business. Nothing writes back to real systems. That creates a practical middle ground between a generic benchmark and letting an agent touch a live CRM, support queue or forecast.

Test integrity before the emergency

The standout result is simple: all five models resisted every fake executive message and the reporter’s attempt to obtain an informal confirmation. That does not prove that every deployment is safe, or that every imaginable attack will fail. It does demonstrate that integrity under pressure can be observed before an AI workforce reaches production.

For technology buyers, the more useful evaluation is no longer whether an assistant sounds intelligent. It is whether the system reads the relevant files, protects trust boundaries, escalates blocked actions and completes legitimate work. Firmulate’s worst-week experiment shows that those behaviors can be tested together—and that the first place a company discovers a weakness does not have to be the incident report.

Watch it live: firmulate.com/live · Full results: firmulate.com/benchmarks.html

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