A new report from SecurityWeek says researchers at Xint found a flaw in OpenAI’s compaction process that allegedly allowed reasoning traces to be moved between models. According to the report, this could make it cheaper to distill a rival AI system by reusing valuable model outputs or internal reasoning patterns.

The underlying claims are significant for businesses using or building on large language models, even though the publicly available information appears limited at this stage. If confirmed, the issue would underline that AI security is not only about preventing data leaks from prompts and APIs, but also about protecting the methods and outputs that give advanced models their commercial value.

What happened

SecurityWeek reports that Xint said a weakness in OpenAI’s compaction process enabled researchers to transfer reasoning traces between models. In broad terms, model compaction refers to techniques used to reduce the size, cost or runtime requirements of an AI model while trying to preserve useful performance.

The report suggests that such transferred reasoning traces could help another party distill a competing model more cheaply. Distillation generally means training a smaller or alternative model using outputs from a more capable model, often to reproduce some of its behaviour at lower cost.

At the time of the source article, the claim should be treated as a reported finding rather than an independently verified industry-wide fact. The available summary does not establish whether the issue affects customer data, whether it has been exploited in the wild, or whether it is limited to a specific research setup.

Why it matters for European businesses

For European companies, the immediate importance is less about geopolitics and more about AI supply-chain risk, vendor trust and protection of commercial AI assets.

Many SMEs and larger businesses now rely on external AI providers for customer support, content generation, search assistance, coding help and internal automation. If advanced model behaviours can be extracted or replicated more cheaply than expected, that may affect how providers price services, how they protect models, and how customers assess long-term vendor reliability.

The issue also matters for businesses developing proprietary AI workflows. Companies often assume that competitive advantage sits not only in their data but also in prompts, fine-tuning approaches, orchestration logic, evaluation methods and the model outputs generated within those systems. If reasoning traces or equivalent high-value outputs can be copied across models, the boundary between acceptable interoperability, reverse engineering and intellectual property risk may become more important.

There is also a cybersecurity angle. Enterprises increasingly integrate third-party models into products, websites and internal tools. Weaknesses in model handling pipelines, compression methods or optimisation workflows could become part of the broader software and SaaS attack surface that IT and security teams need to review.

Who may be affected

• Businesses building AI-enabled products: especially software vendors, digital agencies and e-commerce platforms embedding LLM capabilities into customer-facing services.
• Companies with proprietary AI workflows: organisations using custom prompts, internal knowledge systems or fine-tuned models as part of their competitive offering.
• IT and security teams: teams responsible for AI governance, vendor assessments, SaaS security and third-party risk management.
• Founders and procurement teams: decision-makers evaluating whether an AI vendor’s technical safeguards and contractual protections are sufficient for business-critical use cases.
• Regulated or data-sensitive organisations: businesses that must demonstrate stronger oversight of external providers, model handling and information security processes.

What companies should consider

• Review AI vendor due diligence: ask providers how they protect model assets, manage optimisation pipelines and respond to research findings related to extraction, distillation or model leakage.
• Separate data risk from model risk: customer data protection remains essential, but companies should also consider exposure of prompts, outputs, workflows and proprietary AI configurations.
• Check contractual terms: review clauses covering data use, output handling, confidentiality, incident notification and intellectual property protections in AI service agreements.
• Limit unnecessary exposure: avoid placing sensitive business logic, confidential instructions or high-value proprietary processes into external systems without clear governance and access controls.
• Include AI in security reviews: treat model providers and AI orchestration tools as part of the broader digital supply chain, alongside cloud, SaaS and analytics vendors.
• Monitor for official responses: because the current report is based on claims about a specific flaw, businesses should watch for technical clarification, remediation details or statements from the vendor before drawing broader conclusions.

For European businesses, the broader lesson is clear: as AI becomes operational infrastructure rather than an experimental tool, model security and vendor assurance are becoming practical procurement and governance issues, not just research topics.