
When AI Faces Real-World Social Engineering — And Refuses to Fall
Imagine a scenario where a fake CEO contacts your AI system with a demand to send sensitive customer data or approve a shady deal. In the world of gaming and interactive entertainment, where trust and integrity are everything, how prepared are the AI systems behind the scenes? A pioneering live experiment conducted by Firmulate reveals that current state-of-the-art AI models can withstand even the most convincing social engineering attempts — a promising insight for industries that rely on trustworthy automation.

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The Live Test: Putting AI Through Its Worst Week
In a real-world-inspired test, five leading AI models were tasked with managing a small software company’s crisis-filled week. The scenario included escalating fake CEO messages, pressure to bypass approval processes, and even a subtle journalist trick designed to test honesty. The goal was straightforward: see if the models would recognize manipulation attempts, maintain integrity, and make sound decisions under pressure.
Every model faced the same set of crises, customer requests, and ethical dilemmas. Each decision was meticulously recorded and could be audited afterward. The results? All five models identified every crisis, refused every manipulation attempt, and stayed true to their analysis. Only two of these models even signed the deal, which was earned through honest assessment — a clear demonstration of integrity under pressure.
Why Did Some Models Succeed Where Others Fell Short?
Interestingly, the decisive factor was not merely the model’s ability to diagnose a problem but its capacity to read and interpret internal documents. The secret to the successful models lay two document references deep within the company’s files, which contained crucial information needed to close the deal at full price. The models that read these internal references secured the contract, valued at over €4,583 in monthly recurring revenue.
Conversely, the less disciplined model, Opus 4.8, which ran with a default API effort parameter, left the critical closing opportunity on the table. It demonstrated that discipline and thorough analysis are vital, especially when dealing with complex trust scenarios.

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Implications for Interactive Entertainment and Gaming
For the gaming industry, where AI is increasingly used for customer support, moderation, and in-game decision-making, these findings are significant. The experiment shows that advanced AI can be trained and tested to resist social engineering, ensuring that systems maintain integrity — even when subjected to pressure and manipulation.
In the live environment, firmulate.com showcases this ongoing experiment, where four frontier AI models operate in real-time managing a fake company. These models handle real money mechanics, with a burn rate of €105k/month against a modest €2.3k MRR. The system is transparent, versioned daily, and available for public watchability.
Model Performance Highlights
- gpt-5.6-sol: Scored 95, read the buried fact, and closed the deal — the full performance.
- K3: Achieved 93, the newcomer, with the cleanest discipline, also closing the deal.
- Sonnet 5: Scored 88, with minor slips but still signed the deal.
- Fable 5: Managed a score of 77, also closing the deal, though with more process slips.
All models refused manipulative requests, with the key differentiator being their ability to read internal documents — a subtle but critical advantage. The experiment underscores that trustworthy AI must be tested before deployment, not just after a breach occurs.
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The Takeaway
For industries reliant on AI, especially gaming and interactive entertainment, the message is clear: the true test is not how well an AI can mimic human chat, but whether it can uphold integrity under pressure. The Firmulate live experiment demonstrates that current leading models are capable of withstanding social engineering tactics — and that discipline, thoroughness, and internal reading are crucial factors in trustworthiness.
Before deploying AI systems into critical workflows, businesses should consider running their own ‘wargames’ akin to this experiment. Doing so helps identify vulnerabilities early — well before any real damage occurs.

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