In October 2025, a research collaboration between Anthropic and the Alan Turing Institute exposed one of the most uncomfortable truths in modern AI. Their study, “Poisoning Attacks on LLMs Require a Near-Constant Number of Poison Samples” revealed that large language models (LLMs), no matter how vast or sophisticated, can be compromised even when only a tiny fraction of their training data, sometimes as little as 0.01 percent, is manipulated.

The discovery sounds technical, but its implications reach far beyond data science. It means that even the most sophisticated AI models, trained on immense datasets and deployed by the world’s leading technology companies, can be silently corrupted. Once that happens, the damage is almost impossible to detect or reverse.

For businesses that depend on AI to make or inform critical decisions, this should raise alarm bells. In sectors like finance, healthcare, tax, and law, where every decision must be precise, deterministic and auditable, relying on black-box AI is like trusting a compass that can be quietly nudged off true north.

At Rainbird, we believe this study validates a truth we’ve built our entire platform around: the only way to trust AI is to own your own world model.

The Hidden Vulnerability in Modern AI

The Anthropic–Turing research set out to answer a deceptively simple question: how much data does an attacker need to control to meaningfully distort a large language model’s behaviour? The answer was staggering, almost none.

By corrupting only a handful of training samples, the researchers were able to alter the model’s outputs, introducing subtle biases and behaviours that persisted even after the model was further trained on clean data, meaning that the corruption survived further training intended to correct it. These hidden influences were effectively invisible. The models continued to perform well on benchmarks, and there was no obvious sign of manipulation. Yet under the right conditions, certain prompts or data contexts, they began producing answers that were subtly but systematically wrong.

This wasn’t just a proof of concept. It was a demonstration of how fragile probabilistic systems really are. As AI continues to be trained on open data pulled from the web, code repositories, and crowd-sourced platforms, the opportunity for malicious poisoning only grows. And because an LLM’s behaviour emerges from complex statistical relationships across billions of parameters, rather than any form of decision-making, even its creators can’t pinpoint which parts have been corrupted or how to repair them.

Why Scale Doesn’t Equal Safety

There’s a common assumption that bigger models are safer. That with enough parameters, enough data, and enough fine-tuning, risks like these can be managed away. The Anthropic–Turing study shattered that illusion. Scale doesn’t neutralise risk, it amplifies it.

When a model is trained on trillions of data points, even a few contaminated ones can ripple across the system. Because LLMs generalise patterns statistically rather than logically, corrupted data doesn’t just affect one type of response, it can distort entire sets of outputs. The more complex the model, the harder it becomes to detect where or how things went wrong.

This is the fundamental weakness of probabilistic AI. It doesn’t know what is true; it only predicts what is likely. It doesn’t reason; it approximates. That’s a useful trait when writing poems or summarising news articles. But when applied to regulated decisions, such as assessing tax claims, approving loans, or detecting fraud, it’s an absolute liability.

Determinism: The Foundation of Trust

Rainbird takes a fundamentally different approach. Instead of training on uncontrolled public data, our platform builds reasoning systems from structured knowledge graphs that captures all knowledge sources: human expertise, policies, and regulation – in a deterministic framework.

Determinism means that the same inputs will always produce the same outputs. Every conclusion is based on explicit, logical relationships, and every outcome can be traced, audited, and explained. This approach eliminates the possibility of hidden data poisoning because there is no uncontrolled training data. The reasoning isn’t learned from noise, it’s built from knowledge.

Where probabilistic systems offer fluent mimicry, deterministic systems offer proof. Rainbird’s inference engine ensures that every step in the decision process is recorded and explainable, giving enterprises full control and visibility over how each outcome was reached. In other words, while generative models rely on hope, Rainbird guarantees a decision you can trust. 

Owning Your World Model

The concept of a world model is central to how we think about trustworthy AI. It represents all of the organisation’s knowledge, rules, and expertise that relates to a specific use case. In a Rainbird implementation, this knowledge is encoded in a graph structure and reasoned over by deterministic logic, ensuring that no external data or stochastic influence can alter its behaviour.

When a business owns its world model, it owns the logic that is being leveraged to make its decisions. That means compliance teams can easily audit outcomes and understand their rationale and origins. Regulators can trace how conclusions were reached. And executives can sleep at night knowing their AI systems aren’t silently being influenced by public and unverified data sources.

Contrast this with an LLM, where the outputs are inseparable from the training data and the model weights that encode it. No one can truly “own” such a system, because no one can isolate where its knowledge begins or public training ends. Ownership without visibility is an illusion.

A Turning Point for AI Governance

As AI systems continue to embed deeper into the core of business and government operations, the demand for transparency and auditability will only grow. Regulators are already moving in that direction. The EU AI Act, for example, explicitly requires explainability and traceability for high-risk AI applications.

The Anthropic–Turing study adds weight to the regulatory argument: if even the creators of the world’s most advanced models can’t guarantee integrity, enterprises cannot afford to depend on probabilistic AI for critical reasoning.

The way forward isn’t to abandon generative AI, but to anchor it in deterministic frameworks based on world models. By combining generative interfaces with deterministic reasoning layers, organisations can enjoy the best of both worlds: natural language usability with provable trust.

Building AI the World Can Trust

At Rainbird, we’ve always believed that trust in AI doesn’t come from complexity, it comes from clarity. True intelligence isn’t about predicting what’s likely; it’s about reasoning logically to understand what’s true.

The Anthropic–Turing study has made the stakes clearer than ever. If even the largest models can be poisoned by the smallest contaminations, the industry must rethink what “AI safety” actually means.

Owning your world model isn’t just a technical preference; it’s a moral and operational necessity. It’s how enterprises ensure that the systems guiding their decisions remain aligned, auditable, and secure. Isn’t your institutional knowledge what separates you from the competition? 

The message is simple: you can’t trust what you can’t trace, and you can’t poison a model you own.