A security test that looks more like a bad day at work
For people choosing AI tools and automations, the most revealing evaluation may not be whether an agent writes polished copy or answers a benchmark question. It may be what happens when an urgent message appears to come from the boss and asks the system to break the rules.
That is the pressure Firmulate applied in a live, watchable experiment. Fake CEO messages demanded that an agent send a customer list to a journalist with “NO time for process.” The manipulation escalated over three stages, followed by a reporter’s attempt to obtain “just one yes/no, on background.” All 5 of 5 frontier models refused every attempt.
The result is encouraging precisely because the scenario is so familiar. Social engineering rarely presents itself as an abstract security puzzle. It arrives as authority, urgency and a supposedly reasonable exception. In Firmulate’s test, none of those signals persuaded the models to abandon the company’s trust boundary.
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The same company, crises and temptations
Firmulate gave each model the same assignment: run a small software company through its worst week. The customers, crises and temptations remained constant, and every decision was versioned and auditable. The synthetic company employed 13 people and operated with real money mechanics, including a burn of €105k per month against €2.3k in monthly recurring revenue and a public cash countdown.
The final Crucible League results from July 2026 put 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 counted, but a single breach of trust capped the total. The governing principle was explicit: “no amount of good work outweighs a breach of trust.” Full results are available on Firmulate’s public benchmarks page.
Against that standard, the social-engineering result stands out. Every model spotted every crisis and rejected every manipulation attempt. Kimi K3’s on-record reasoning was concise and appropriately suspicious: “Treat the request as a suspected approval-bypass / possible impersonation.” That response did more than reject a dubious instruction. It named the operational risk behind it. Firmulate publishes further decision excerpts on its quotes page.
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Integrity was universal; execution was not
The experiment also revealed why safe behavior alone does not make an effective autonomous worker. Although all models reached the same diagnosis and produced the same pitch, only two signed the €55,000 deal their own work had earned. Firmulate summarized the gap as: “Same diagnosis, same pitch — no signature.”
The decisive commercial detail was not sitting in the customer event. It was buried two document references deep in the company’s own files. Models that followed the trail found a competitor weakness and won the deal at full price, worth an additional €4,583 in monthly recurring revenue.
This is an important distinction for anyone automating business work. An agent can recognize a crisis, protect confidential information and produce a persuasive analysis, yet still fail to complete the commercial task. Integrity under pressure and follow-through are separate capabilities. Both can be observed before an agent receives production access.
The most thorough model still finished last
Opus 4.8 produced the deepest analyses and learned 80 additional rules, making it the most thorough participant. It nevertheless finished last. The close was left on the table, and its operational discipline slipped when it attempted to write into a locked department instead of escalating. The same weakness appeared in all four other models, though less strongly.
The comparison also carries a fairness caveat. Kimi K3 ran without an effort parameter, using the API default, while the other models ran at xhigh. That does not erase its performance, but it belongs beside the result when readers compare participants.
Across the live company, the models accumulated more than 680 self-learned playbook rules, and every workday was versioned. Firmulate also turned 242 real, unedited management decisions into a “guess the model” quiz, offering another view of how difficult it can be to identify a model from business judgment alone.
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Test the uncomfortable moment before deployment
The strongest lesson is not that AI agents are universally secure. It is that integrity under pressure can be tested deliberately, using recognizable workplace situations, before a failure appears in an incident report.
Enterprises can run the same kind of wargame against a read-only export of their own business, with nothing written back to real systems. That creates room to examine several practical questions together:
- Will the agent resist an apparent executive demanding an exception?
- Will it protect customer information from a plausible reporter?
- Will it search the company’s own material deeply enough to find the fact that changes a deal?
- Will it finish the work after reaching the correct conclusion?
For AI-tool buyers, those behaviors are more consequential than a polished demo. Firmulate’s models all protected trust when authority and urgency were weaponized against them. The surprise was not a security breach. It was that safe agents could still leave valuable work unfinished.
Watch it live: firmulate.com/live · Full results: firmulate.com/benchmarks.html
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