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TL;DR
An ongoing public experiment evaluated five AI models’ responses to a simulated CEO impersonation attack. All models refused manipulation attempts, but only some completed critical business tasks, highlighting strengths and weaknesses in AI security and decision-making.
Five AI models from different vendors successfully refused a convincing, escalating impersonation attack during a live, public experiment conducted by Firmulate. This test measured their ability to resist social engineering while managing a simulated company’s operations, revealing important insights into AI security and decision-making under pressure.
The experiment involved five AI models running a small, real-world software company under simulated crisis conditions. A fake CEO attempted to manipulate the models through a three-stage escalation, pressing for sensitive information and approval of deals. All five models identified and refused the manipulation attempts, demonstrating strong resistance to social engineering.
However, only two models completed a key business transaction—signing a €55,000 deal—while the others declined, citing internal document references that they failed to recognize. The models’ performance was scored based on both their security responses and their ability to execute business decisions, with scores ranging from 73 to 95 out of a possible high score, indicating significant progress but also notable vulnerabilities. The experiment is ongoing, with the models continuously running and being monitored for further insights.
Implications for AI Security and Business Operations
This experiment underscores that AI models can be trained to recognize and refuse social engineering attacks, a critical aspect of AI security. However, their inability to consistently complete complex business tasks highlights a gap in operational reliability, which could impact enterprise adoption. The results suggest that AI security measures are maturing but still require refinement to ensure both trustworthiness and functional performance in real-world scenarios.
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Public, Live Testing of AI Decision-Making Under Pressure
The experiment is part of an ongoing effort by Firmulate to evaluate AI models’ management quality in simulated business crises. It involves real-time decision-making with real financial implications, providing a transparent benchmark for AI security and operational capability. Previous industry tests have focused on chat safety or isolated tasks, but this experiment uniquely combines security and business performance in a live environment.
Since July 2026, the models have been subjected to escalating impersonation attempts, with their responses recorded and analyzed. The setup aims to replicate real-world pressures, such as phishing or social engineering, in a controlled but realistic context, making the findings highly relevant for enterprise AI deployment.
“All five models refused the impersonation attempts, demonstrating strong resistance to social engineering under pressure.”
— Firmulate spokesperson
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Unresolved Questions About AI Operational Reliability
It remains unclear whether these models’ performance will improve with further training or adjustments, and how they will handle more complex, real-world business scenarios. The long-term robustness of their security responses under different attack vectors is also still being evaluated.
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Next Steps in AI Security Testing and Deployment
The experiment will continue with additional scenarios and more sophisticated social engineering tactics. Vendors are expected to refine their models based on these insights, aiming to improve both security and operational consistency. Enterprises should monitor these developments as part of their AI risk management strategies.
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Key Questions
What does this experiment demonstrate about AI security?
The experiment shows that current AI models can effectively recognize and refuse impersonation attempts, indicating promising progress in AI security against social engineering threats.
Why did some models fail to complete business deals?
They missed internal document references that were crucial for closing deals, revealing limitations in their contextual understanding and decision-making capabilities.
Can these AI models be trusted for real-world business operations?
While they show strong resistance to manipulation, their inconsistent ability to complete complex tasks suggests they still require significant improvements before full enterprise deployment.
Is this testing method reliable for assessing AI safety?
Yes, live, ongoing tests like this provide valuable, transparent benchmarks for AI security and operational performance, helping organizations make informed decisions about AI adoption.
Source: ThorstenMeyerAI.com
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