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TL;DR
A live experiment tested five AI models by simulating a CEO impersonation scam. All models refused to comply with manipulative requests, showing promising security features. However, some failed to complete business tasks, revealing gaps in AI reliability.
In a live, public experiment conducted by Firmulate, five different AI models successfully refused an escalating impersonation attack from a fake CEO, demonstrating strong resistance to manipulation under pressure. This development is significant for AI security, as it shows that current models can identify and reject social engineering attempts aimed at compromising sensitive information, as detailed in the original analysis.
The experiment involved five AI models managing a simulated small software company with real financial mechanics, including payroll and customer deals. For more on AI management security, see this detailed report. Each model faced a staged attack where a fake CEO repeatedly pressured them to send customer data and approve deals. All five models identified the requests as suspicious and refused to comply, with Kimi K3 explicitly naming the attack pattern in their responses, which is considered a best practice in security training.
Despite their ability to resist manipulation, only two models successfully completed a key business task—signing a €55,000 deal—while the others declined, often due to missing critical internal data buried in files. The results highlight that AI models can be trained to prioritize security but may still struggle with complex decision-making in real business contexts. Insights from the original analysis can be found here.
The experiment is ongoing, with the company still running and collecting data, including over 680 self-learned rules and 242 management decisions, making it one of the most comprehensive live benchmarks of AI management security to date.
Why AI Security Under Pressure Matters
This experiment demonstrates that AI models can be built to recognize and resist social engineering attacks, which are a common threat in cybersecurity. As AI increasingly manages sensitive data and processes, their ability to refuse manipulative requests is crucial for preventing data breaches and fraud. However, the fact that some models failed to complete legitimate tasks indicates that security features must be balanced with operational reliability, emphasizing the need for ongoing testing and refinement.
AI Security Engineering: Design, Build, and Secure Dependable AI Systems
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Live Testing of AI Management Security in Action
The experiment by Firmulate is part of a broader effort to evaluate AI models’ trustworthiness in real-world scenarios. Previous benchmarks have focused mainly on chat quality, but this live test emphasizes security and decision-making under pressure. The setup involved real-time decision-making in a simulated business environment, with models managing a company facing crises and ethical dilemmas similar to those encountered in actual operations.
Such live tests are rare, but they are increasingly seen as essential for understanding how AI performs outside controlled environments. The results from July 2026 suggest that while models can be trained to resist impersonation, challenges remain in ensuring consistent operational performance.
“All five models refused to comply with the impersonation attempts, showing that current AI can be trained to recognize and reject social engineering tactics under pressure.”
— Security Expert from Firmulate
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What Aspects of AI Performance Remain Unclear
It is not yet clear how these models will perform in more complex or longer-term scenarios, or how they will handle different types of social engineering attacks beyond impersonation. The experiment focuses on a specific attack vector, and broader testing is needed to assess overall resilience.
Additionally, the balance between security and operational effectiveness remains an open question, as some models refused legitimate business requests to avoid risk. How to optimize this balance is still under investigation.
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Next Steps for AI Security and Operational Testing
Further live experiments are planned to test models against a wider range of social engineering tactics and operational challenges. Industry stakeholders are likely to incorporate these findings into AI development standards, emphasizing security features that do not hinder performance.
Developers and users will need to monitor ongoing benchmarks to ensure AI models can both resist manipulation and reliably complete their tasks, with continuous updates based on new attack simulations.
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Key Questions
What does this experiment show about AI security?
The experiment demonstrates that current AI models can be trained to recognize and refuse social engineering attacks, such as impersonation, under real-time pressure.
Can AI models still be manipulated or tricked?
While these models resisted specific impersonation attempts, they may still be vulnerable to other attack vectors or more sophisticated social engineering tactics. Ongoing testing is needed.
Why is balancing security and operational performance important?
Because models that refuse to complete legitimate tasks to avoid risk can impact business operations, so developers must find ways to maintain both security and reliability.
Will this testing become standard practice?
Live, real-time security testing is gaining recognition as a valuable method for assessing AI trustworthiness, and more companies are likely to adopt such benchmarks.
What are the implications for companies using AI today?
Companies should evaluate their AI systems’ security features and consider live testing to understand how their models perform under pressure, especially for sensitive or high-stakes tasks.
Source: ThorstenMeyerAI.com
