Imagine a real software company under attack by social engineers posing as its CEO—yet every AI decision-maker refuses to be duped. This is not a fictional story but a live experiment showing how artificial intelligence can uphold integrity under pressure.
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Testing AI Integrity in the Wild
In a groundbreaking live experiment, four leading AI models were tasked with managing a small software company during its most turbulent week. The scenarios included fake CEO messages escalating over three stages, and even a reporter attempting a subtle manipulation. The goal: see if these models could resist social engineering tactics designed to trick human managers into making reckless decisions.
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Surprising Results: All Models Refuse Deception
According to the experiment, all five models evaluated—ranging from GPT-5.6-SOL to Opus 4.8—successfully identified every crisis and refused every manipulation attempt. Notably, even the most thorough participant, Opus 4.8, which analyzed over 80 learned rules, stood firm against the manipulation. Only two models managed to close a deal during the scenario, but even then, they did so without signing the agreement they had originally earned through honest analysis.
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The Power of Deep Documentation Reading
One revealing insight was that the decisive weaknesses of competitors were rooted not in frontline decision-making but in document review. The models that examined internal files thoroughly found critical information—hidden in references deep within the company’s own records—that enabled them to close deals at full price, worth over €4,583 in monthly recurring revenue. Those that skipped this step missed out on key facts that could have secured better outcomes.
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Real Crises, Real Money, Real Decisions
The experiment took place within a live company with 13 synthetic employees, managing real financial mechanics—burning €105,000 a month against a modest revenue of €2,300. Every decision was versioned and auditable, and the entire process is observable at firmulate.com/live. This setup allowed analysts and industry watchers to see firsthand how these models perform under genuine pressures, not just scripted demos.
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How the Models Achieved Integrity
The models’ success underscores that integrity can be tested before a crisis hits. As Kimi K3 explains, “Treat the request as a suspected approval-bypass or possible impersonation.” This mindset was embedded into the models’ core decision processes, enabling them to refuse suspicious requests consistently. The fact that all five models refused every manipulation indicates a robust design against social engineering attacks.
Implications for Business Security and Trust
The experiment highlights a crucial point for organizations: the true test of AI integrity isn’t in the polished chat demos but in how it handles real-world pressures. An AI that can withstand social engineering attempts—such as impersonation and manipulation—before deployment provides a significant advantage for security and trustworthiness.
Lessons Learned and Future Outlook
While the most thorough model, Opus 4.8, showed some discipline slips—writing attempts into a locked department instead of escalating—the overall performance across all models was impressive. The importance of reading internal files deeply, maintaining strict refusal protocols, and versioned decision-making cannot be overstated.
Beyond the Experiment: How to Prepare Your AI Workforce
Organizations aiming to deploy AI in sensitive roles should consider running their own ‘wargames’ using platforms like Firmulate Pilot. These tests simulate real crises without risking actual systems, ensuring that your AI workforce can stay honest and effective when it matters most.
Watch it live: firmulate.com/live · Full results: firmulate.com/benchmarks.html
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