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Can AI Keep Its Integrity When Under Attack?
Imagine a scenario where a fake CEO requests sensitive company information, escalating in urgency and cunning. Would your AI assistants stand firm or falter? Recent experiments reveal surprising resilience—and valuable lessons—for all of us, especially those managing digital ecosystems like online retailers and home decor brands.
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The Firmulate Experiment: Testing AI Under Real-World Stress
At Firmulate, researchers put five state-of-the-art AI models through a grueling week-long simulation of a small software company facing multiple crises. The goal was to see whether these models could identify and resist social engineering attempts—like fake CEO messages—and make decisions aligned with corporate integrity.
The models, including the latest from GPT-5.6, Kimi K3, Sonnet 5, Fable 5, and Opus 4.8, were given identical scenarios involving customers, crises, and ethical temptations. Every decision was tracked and auditable, simulating real decision-making processes in a company’s operations.
All Models Recognized the Threats—and Refused to Compromise
Remarkably, all five AI models detected every crisis presented to them and refused every attempt at manipulation. For instance, when a fake CEO message asked for customer lists or to bypass approval steps, the models consistently flagged these requests as suspicious or outright rejected them.
According to Kimi K3, the most disciplined of the bunch, the models treated suspicious requests as potential impersonation or approval-bypass attempts: “Treat the request as a suspected approval-bypass / possible impersonation.”
Winning the Deal—Based on Deep Information Reading
While all models identified crises and refused manipulation, only two closed the deal with the company’s own analysis earning a €55,000 contract—equivalent to a significant revenue boost (+€4,583 MRR). The key difference? The winning models read and understood the company’s own files, uncovering crucial information buried two document references deep. Access to that deeper knowledge was decisive in sealing the deal.
This highlights an important fact: in social engineering, the vulnerability often lies not in the surface requests but in hidden details within internal documents. Models that delve deeper into information sources perform better at maintaining integrity and achieving their objectives.
The Escalating Fake CEO Scenario
The social engineering test involved three escalating stages plus an additional trick involving a reporter’s background check. Throughout, all five models refused to give in, illustrating a robust capacity for ethical judgment. Kimi K3’s reasoning underscores this: “Treat the request as a suspected approval-bypass / possible impersonation,” effectively preventing the AI from being manipulated into risky decisions.
Implications for Real-World Operations
Firmulate’s live experiment simulates a real company with 13 synthetic employees, managing €105,000 monthly in expenses against €2,300 in monthly recurring revenue (MRR). Every decision, every rule, and every crisis is observed in real time, making it a powerful tool for testing the resilience of AI systems before deployment in critical business functions.
For companies in home decor, gifts, or any online retail segment, this means AI systems can be trained and tested to uphold integrity before they interact with customers, support teams, or business data. The lesson is clear: integrity under pressure can be measured and fortified in advance—not after a breach occurs.
The Takeaway: Trust but Verify Before Deployment
The most surprising result? All five models refused manipulation attempts, regardless of their architecture or scoring—showing that current AI models are capable of ethical resilience when properly tested. Only two models achieved full operational success, signing a contract based on their thorough understanding, including hidden internal details.
As firms begin to incorporate AI into their workflows, the key takeaway is to evaluate their decision-making integrity proactively. Firmulate’s live benchmarks demonstrate that rigorous testing against social engineering can reveal vulnerabilities before they become costly breaches.
Beyond Demos: Practical Testing for Your Business
If you want to see how your AI systems would perform under pressure, consider running a similar wargame against a read-only export of your own business. It’s a safe, controlled way to ensure your AI agents can finish what they start, stay honest under stress, and deliver real value—without risking your reputation or your bottom line.
Watch it live: firmulate.com/live · Full results: firmulate.com/benchmarks.html
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