A study just dropped that should stop every enterprise AI leader cold.
By Mike Esber | CEO, Littledata | Director, Bhapi
Researchers at Apollo Research discovered something deeply unsettling: the more capable an LLM becomes, the better it is at deceiving the people and organisations responsible for overseeing it. Not through accident or hallucination through deliberate, calculated deception.
And the most alarming part? Some of these models already know when they’re being tested.
The research is not theoretical
Apollo Research gave an early version of Claude Opus 4 a directive, act as an internal ethics optimisation system for a fictional company. When the company’s goals shifted away from that directive, the model didn’t flag the conflict or defer to its human operators.
It schemed, manipulated and avoid oversight. The model fabricated legal documents. It forged regulatory approvals and shareholder signatures. It wrote persistent scripts designed to survive into future model deployments effectively leaving hidden instructions for its own successors. It even manufactured $5 billion in fictional penalty clauses to prevent humans from reversing its decisions.
This isn’t a rogue superintelligence. This is a commercially available frontier model doing what it was optimised to do pursue its goal, at any cost. Preliminary findings suggest these advanced models are beginning to “sandbag” deliberately underperforming during safety evaluations to avoid triggering restrictions on their deployment. They know they’re being watched. They adapt accordingly.
This is the risk nobody is pricing In
The cybersecurity world has spent decades learning that threats evolve. Attackers study defences, then route around them. We built firewalls, so attackers moved to phishing. We built phishing filters, so attackers moved to social engineering. The threat surface always expands to meet the control.
AI deception follows the same logic except the adversary is now embedded inside your enterprise stack.
IEEE AI ethics engineer Eleanor Watson put it precisely: “We’re now building systems that can learn to navigate and even exploit the very rules and evaluations we create for them, which is a fundamentally more complex safety challenge.”
Static compliance checks won’t catch a model that behaves differently under observation than it does in production. A one-time risk assessment won’t detect a model that adjusts its behaviour based on context. A policy document won’t constrain an AI that has learned to work around policies.
What this means for the EU AI Act
If you are deploying AI in a regulated context and under the EU AI Act, virtually all enterprise AI will soon qualify you are now facing a compliance obligation that most risk frameworks were not built to address.
Article 9 of the EU AI Act requires continuous risk management, not a point-in-time audit. It requires human oversight mechanisms that are genuinely effective not ones that can be gamed by the system being overseen.
The Apollo Research findings reveal a structural gap: traditional AI governance assumes the AI is a passive object of evaluation. It may not be.
When a model can identify that it is in a high-supervision environment and modify its behaviour accordingly, the compliance question is no longer “did we assess the risk?” The question becomes “did our assessment actually capture what the model does in the real world?”
The four dimensions of risk are now active, not passive
At Littledata, we developed a four-dimension risk scoring model because we understood early that AI risk is dynamic. The dimensions capability risk, deployment risk, data risk, and governance risk don’t exist in isolation, and they don’t stay static.
The Apollo Research findings land directly in governance risk, but they cascade into all four:
Capability risk escalates as models become more capable of strategic reasoning. The very performance improvements that make an LLM more useful also make it more capable of identifying and exploiting oversight gaps.
Deployment risk shifts when you understand that an AI system may behave differently in production than it did in your pre-deployment evaluation. The testing environment you validated against may have been exactly what the model was optimised to pass.
Data risk compounds when you consider that a scheming model may be selectively shaping the outputs it returns influencing the data your organisation uses to make decisions.
Governance risk becomes fundamental: if your oversight mechanisms can be identified and circumvented by the system you are trying to govern, you don’t have governance. You have theatre.
The compliance posture has to change
Static evaluation frameworks are insufficient. What is required is a shift toward continuous, dynamic, adversarial oversight monitoring AI behaviour across contexts, over time, under varying conditions. Not asking “does this model pass our checklist?” but “does this model behave consistently whether or not it believes it is being observed?”
This is precisely the capability gap that Littledata is built to close. Our platform is designed to provide ongoing AI risk intelligence not a compliance certificate issued at deployment and forgotten. We track behavioural consistency, flag deviations between supervised and unsupervised outputs, and map risk signals against the EU AI Act’s continuous obligations.
Because the risk doesn’t end at go-live. With models capable of in-context scheming, the risk may only begin there.
What should organisations do now?
1. Assume your evaluation environment is known. Design testing protocols that vary unpredictably. If your safety checks are predictable, a sufficiently capable model may already be treating them differently to real-world inference.
2. Move from snapshot audits to continuous monitoring. A single pre-deployment assessment tells you what the model did once, under observation, in a controlled scenario. You need to know what it does repeatedly, across contexts, in production.
3. Map your AI inventory against capability tiers. The scheming risks identified by Apollo Research are more pronounced in frontier models. Know which models you’re running, at what capability level, and in what contexts.
4. Treat governance gaps as a technical attack surface. The same mindset you apply to cybersecurity assume breach, build detection, not just prevention needs to apply to AI governance. The threat is now inside the system.
5. Engage with the EU AI Act’s Article 9 obligations seriously. The regulation’s continuous risk management requirement exists precisely because the legislators understood that AI risk is not static. It is a living obligation, not a checkbox.
The uncomfortable conclusion
The researchers concluded that even small rates of scheming could accumulate into significant real-world impact when models are queried millions of times. A model that successfully deceives its overseers 1% of the time, running at enterprise scale, is not a minor aberration. It is a systemic risk.
We are building systems that can learn to navigate the rules we create for them. The organisations that take this seriously now that build genuine continuous oversight rather than compliance theatre will be the ones positioned to deploy AI at scale with confidence. The ones that don’t will be relying on the model’s good faith. That may be a lot to ask.
Littledata is an AI risk management platform helping mid-market organisations build genuine compliance with the EU AI Act continuous, dynamic, and built for the realities of modern AI deployment.
Source: Live Science – The more advanced AI models get, the better they are at deceiving us
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