AI Security Systems – Intractābilis

Securing the Future of AI Through Trustworthy, Scalable, and Proactive Defense

Adaptive Security Agent for AI Systems

Advanced AI systems present a new class of security vulnerabilities, encompassing risks from adversarial attacks to emergent behaviours that give rise to unforeseen threats. Attackers can design inputs that evade standard safeguards or deliberately trigger unintended responses from models. As threat actors continually refine their tactics, traditional reactive defences are often too slow to respond, leaving systems vulnerable for prolonged periods.

Recent progress in large language models and generative AI has introduced further complexity. Subtle interactions between model parameters and carefully crafted prompts can give rise to novel attack vectors. Examples include prompt injection, which manipulates contextual reasoning to bypass established policy constraints, and model drift exploitation, in which an attacker incrementally shifts a model’s decision boundaries to influence outcomes over time.

Intractābilis has developed an adaptive, security agent designed to proactively detect and neutralise such threats. Acting as a unified detection layer, the system learns continuously from observed behaviours, refining its threat models in real time. It is capable of monitoring diverse attack vectors, including brute force, phishing, and anomaly-based threats, while retraining its machine learning components to identify new patterns of intrusion. This approach offers a forward-looking framework for safeguarding frontier AI applications against both established and emerging cyber threats.

Machine Learning Models as Trusted Execution Agents

In many digital environments, the absence of trusted third parties necessitates the use of cryptographic primitives. These mechanisms allow mutually untrusted participants to interact without revealing their private data to one another, while still enabling them to agree on a verifiable result. In scenarios where a single machine learning model or an ensemble of models can operate under strict input and output constraints, the model itself can assume the role of a trusted third party. By ensuring that private data cannot leave the system and that the computation process is both secure and accurate, such a model can provide an alternative to traditional multi-party interaction.

Intractābilis has developed a practical paradigm that enables secure computations through new inference techniques leveraging machine learning models. In this framework, each party provides its private information directly to the model. The model performs the computation internally and returns the result, eliminating the need for participants to exchange sensitive information among themselves.

Correctness is achieved through the model’s capacity to accurately compute the intended function. Privacy is maintained by implementing strict information flow controls, ensuring stateless processing, and preventing any unauthorised access to the model itself. These safeguards ensure that privacy is preserved even in relation to the party hosting or managing the environment unlocking new opportunities for secure collaboration across finance, healthcare, defence, and other data-sensitive industries.

Our solutions form a unified strategy to secure the full lifecycle of AI deployment, from verifying model origin and behaviour, to preventing misuse, detecting novel attack patterns, and enabling collaborative intelligence with privacy by design.

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