LLM security in 2026: Risks, threats and how to protect your systems

What is LLM security?

LLM security is the set of policies, practices and technologies used to protect large language models from misuse and attack, while keeping the data they process secure, confidential and available. It differs from traditional cybersecurity in three important ways:

• LLMs work with vast, unstructured datasets: This creates risks such as data poisoning that are less common in conventional software.
• LLMs are difficult to explain: Traditional applications can usually be audited line by line. With billions of model parameters, understanding exactly why an LLM produces a particular response is far harder.
• LLMs are probabilistic, not deterministic. The same prompt can produce different responses, making unpredictability an important security consideration.

This means that traditional cybersecurity controls alone aren't enough. Firewalls, endpoint protection and network monitoring remain essential, but they weren't designed to detect threats such as prompt injection or poisoned training data.

For more on the technology behind LLMs, see our explainers on how large language models work and the mechanics of LLMs.

Why LLM security matters for UK businesses in 2026

The scale of the cyber security challenge is significant. DSIT’s Cyber Security Breaches Survey 2025/2026 found that 43% of UK businesses, around 612,000 organisations, experienced a cyber security breach or attack in the previous 12 months. AI introduces another layer of risk: while around one-third of UK businesses are using, adopting or considering AI, only about a quarter of these have cyber security measures in place to manage AI risks.

With this being said, the message is clear for CISOs and IT Directors: AI adoption is moving faster than AI governance.

So, why does LLM security matter for UK businesses in 2026? Here are some key reasons:

Financial risk

A security breach involving an LLM carries direct and indirect costs, such as regulatory fines, litigation, discontinued services, and downtime that hits revenue immediately. Recovery adds further costs like system audits, infrastructure upgrades, and staff retraining, all of which pull resource away from growth.

In fact, DSIT's survey found that reported revenue loss and reputational damage from breaches have both increased year on year, even though the overall breach rate has stayed broadly flat. The message for business leaders is clear: cyber risk may not be becoming more common, but the financial consequences of an incident are becoming more costly.

Compliance and regulatory risk

Organisations using LLMs must align with regulatory frameworks, such as the EU’s AI Act, GDPR, or sector-specific compliance mandates. Failure to comply with these regulations can result in breaches where sensitive customer or business data is exposed, triggering severe penalties for non-compliance. For instance, under GDPR regulations, companies can be fined up to €20 million or 4% of their annual global turnover, whichever is higher, for non-compliance.

LLM security, adhering to regulatory frameworks, not only protects organisations’ data but also avoids costly legal consequences. With proper encryption, access controls, security measures and policies in place, it ensures that companies remain compliant in their LLM usage and operations.

Reputational risk

Data breaches and security incidents erode customer trust quickly. Robust LLM security, encompassing encryption, authentication protocols, and regular vulnerability assessments, reassures organisations, customers and partners that their data is handled responsibly, and visible alignment with recognised frameworks signals genuine commitment rather than a bolted-on policy.

Intellectual property and data exposure risk

LLM models frequently handle proprietary algorithms, strategic plans and other confidential business data. A breach can expose that intellectual property to competitors or malicious actors. Strict access controls, built on the principle of least privilege, remain the most effective way to keep this risk contained.

Worried about where your data ends up when employees practice shadow AI? OneAdvanced AI keeps your data UK-hosted, encrypted, and under your organisation's control. Book a demo of OneAdvanced AI

The OWASP top 10 LLM risks (2025 Edition)

The OWASP Top 10 for LLM Applications (released in 2023 and updated in late 2024) is a widely used industry reference for understanding and managing security risks in LLM-powered applications. Maintained by the OWASP GenAI Security Project, the 2025 edition introduces two new risk categories and reorganises several existing ones based on community feedback and emerging threats.

Here’s what’s changed, why it matters and what you should focus on.

LLM01: Prompt injection

Prompt injection remains the top-ranked risk for the second edition running, because LLMs process instructions and data through the same channel, with no reliable way to separate the two. An attacker can seed a forum, document, or webpage with hidden instructions; if someone later asks an LLM to summarise that content, the model may follow the embedded instruction rather than treat it as text to summarise. For example, redirecting a user to a phishing site.

LLM02: Sensitive information disclosure

Because Large Language Models are trained on vast datasets and can retain patterns from that data, cleverly constructed prompts can sometimes extract personal, proprietary or confidential information the model was never meant to reveal. This is a core LLM data privacy concern for any organisation feeding customer or employee data into a model.

LLM03: Supply chain vulnerabilities

Most LLM deployments rely on third-party plugins, APIs, fine-tuned models and vendor integrations. Each connection point is a potential weak link, and a compromised dependency can give an attacker a route into much broader systems.

LLM04: Data and model poisoning

Attackers can tamper with training or fine-tuning data to skew a model's outputs or plant hidden vulnerabilities, compromising the integrity of everything the model subsequently produces.

LLM05: Improper output handling

When an LLM's output is passed to another system without being validated or sanitised, the user trust the model for safe content. That trust can be exploited. For example, malicious JavaScript generated by an LLM could execute in a web browser, while unsafe SQL could be passed to a database. This risk often works alongside prompt injection, where an attacker manipulates the model into generating harmful output that the application then executes.

LLM06: Excessive agency

Giving an LLM autonomous decision-making power over operationally critical tasks is efficient, but risky if the model misinterprets an instruction or acts outside its intended scope. Human-in-the-loop remains essential for high-stakes, high-autonomy use cases.

LLM07: System prompt leakage

Many applications assume the system prompt (the instructions that shape how a model behaves) stays hidden. Real-world exploits have shown this assumption doesn't hold: a leaked system prompt can expose internal logic, business rules, or even credentials to an attacker.

LLM08: Vector and embedding weaknesses

As Retrieval-Augmented Generation (RAG) becomes more widely used, vector databases and embeddings create new security risks. Attackers could poison a vector store so malicious content is retrieved in legitimate queries, exploit weak access controls to access data across tenants, or manipulate embeddings to reduce the accuracy and trustworthiness of AI-generated results.

LLM09: Misinformation

LLMs can sound confident while being wrong by hallucinating facts, inventing citations, or producing polished but incorrect answers. The 2025 list renamed this category from "Overreliance" to sharpen the focus: the risk isn't only that users trust the output too much, it's that the model itself generates and spreads false information.

LLM10: Unbounded Consumption

This expands the old ‘Model Denial of Service’ category to cover both availability attacks: ‘overwhelming a system with requests’ and ‘denial of wallet’ risk, where uncontrolled resource use drives up costs unexpectedly.

Two further risks from the 2023 list remain highly relevant even though they're no longer standalone categories: model theft, where a proprietary model or its underlying IP is stolen and reused by competitors or bad actors, and insecure plugin design, where poorly vetted third-party plugins introduce malware or compromise data integrity. Both are best addressed through the access controls and supply chain vetting covered in the next section.

Building a strong LLM security strategy: Key components

A strong LLM security strategy needs to protect the entire AI lifecycle – from the data and models you use to the systems and users that interact with them. The following key components can help you build a more secure, governed and resilient LLM environment.

Data encryption and secure storage

Encrypt data at rest and in transit using recognised standards such as AES-256, and manage encryption keys with strict, role-limited access. This protects the datasets used both to train and operate your models and keeps data integrity intact across the pipeline.

Access controls and identity management

Role-based access control (RBAC) and multi-factor authentication (MFA) ensure only authorised users, applications and services can interact with your LLM systems. Single sign-on (SSO) and federated identity streamline this without weakening it.

Secure development practices for LLMs

Build security into the development lifecycle from the start. Use code reviews, automated vulnerability scanning and penetration testing to identify risks before deployment. Use trusted libraries, maintain visibility across the software supply chain, and implement secure update processes for models already in production.

Incident response planning for AI systems

An AI-specific incident response plan should cover detection, containment, root-cause analysis and communication protocols, with regular drills so the organisation isn't improvising during a real incident. This matters even more given that AI-driven attacks, including automated vulnerability scanning and AI-generated phishing, can also overwhelm systems, making denial-of-service protection part of a resilient AI operating model.

Governance aligned to the NCSC Guidelines for Secure AI System Development

The UK's National Cyber Security Centre, working with more than 20 international agencies, structures AI security across four lifecycle stages: secure design (threat modelling, risk understanding), secure development (supply chain security, documentation), secure deployment (protecting infrastructure and models, responsible release), and secure operation and maintenance (logging, monitoring, update management). Mapping the practices above onto these four stages gives you a defensible, internationally recognised structure and a natural foundation for a broader AI governance framework.

Best practices to mitigate LLM security risks (checklist)

Use this checklist as the operational backbone of your LLM security framework. It provides the evidence boards, auditors and security teams need to track where they stand and from where to start.

☐Encrypt data both in transit and at rest.

☐Vet, authenticate and audit training and retrieval data sources. Exclude untrusted datasets.

☐Anonymise or pseudonymise sensitive data during training and pre-processing.

☐Conduct regular penetration testing and vulnerability scanning of LLM APIs.

☐Continuously monitor system behaviour, access logs and usage patterns, with automated alerts for suspicious activity.

☐Apply least-privilege access and require human oversight for high-autonomy use cases.

☐Vet third-party plugins, APIs, and require appropriate security standards from vendors and partners.

☐Map security controls to recognised frameworks, including the OWASP Top 10 for LLMs, NCSC guidance, NIST AI RMF and ISO/IEC 42001.

Ready to see enterprise-grade LLM security in action? Talk to our team about how OneAdvanced AI is built for secure, compliant AI adoption. Book a demo

How UK and EU regulation is shaping LLM security

The regulatory picture for generative AI has moved fast, and UK organisations need to track three separate strands.

NCSC Guidelines for Secure AI System Development

The UK-led framework described above, now endorsed by agencies across more than 20 countries, structured around design, development, deployment, and operation.

ICO's five steps to protect against AI-powered cyber threats

Published in 2026, this focuses on five areas: understanding AI-enabled threats, using layered security, limiting network access, strengthening monitoring and incident response, and protecting personal data through measures such as minimisation, encryption and pseudonymisation. The ICO also points organisations toward the government's AI Cyber Security Code of Practice and the existing Cyber Essentials and Cyber Governance Code of Practice as a baseline.

EU AI Act

The EU AI Act introduces phased requirements. Key milestones include AI literacy, and prohibited practices from February 2025, general-purpose AI obligations from August 2025, and transparency requirements from 2 August 2026. Most high-risk AI systems now have until December 2027 to comply, with some product-embedded systems extending to 2028. UK organisations operating in or selling to the EU should prioritise the 2026 transparency requirements and plan ahead for high-risk system compliance.

How to choose a secure LLM provider

For CISOs, DPOs and IT Managers evaluating vendors, these five questions cut through most of the noise:

Data sovereignty and hosting location

Question: Is your data hosted in the UK or EU, and does the provider guarantee it stays there?

Encryption standards and key management

Question: Is data encrypted at rest and in transit, and who controls the keys?

Access control, authentication and audit logging

Question: Can you enforce RBAC, MFA, and see a full audit trail?

Compliance certifications

Question: Does the provider align with NCSC guidance, ISO/IEC 42001, and UK GDPR, and can they evidence it?

Incident response support and transparency

Question: What's the provider's commitment if something goes wrong, and how quickly will you be told?

OneAdvanced AI:  A secure, UK-hosted LLM platform

OneAdvanced AI is the UK's first LLM-based tool hosted exclusively on a private UK platform, built for organisations that need the productivity of generative AI without exporting sensitive data to the public internet. It sits within OneAdvanced IQ, our connected, trusted, intelligent system of work.

• Connected because it's built on unified workflows and business context rather than a bolted-on chatbot
• Trusted because it's secure and sovereign by design, with enterprise-grade cyber security and sector-aligned compliance built in
• Intelligent because AI-driven insight and automation are embedded directly into the flow of work, not layered on top of it.

Data hosted exclusively in the UK, full encryption, and organisation-controlled identity management mean OneAdvanced AI is fine-tuned to your organisation's tone and context without your data ever leaving your control or being used to train the underlying model. As the threat landscape evolves, we continue to evaluate emerging open-source LLM developments, so our customers stay ahead of the risks covered in this guide, without compromising on UK data sovereignty.

Ready to move from evaluating LLM security risks to actually closing them? Book a demo of OneAdvanced AI

Frequently Asked Questions (FAQs)

How is LLM security different from traditional cybersecurity?

Traditional cybersecurity assumes deterministic, auditable systems. LLMs are trained on vast, often unstructured datasets, are much harder to fully explain, and behave probabilistically. LLM-specific risks like prompt injection and data poisoning need their own controls, on top of standard cybersecurity practice.

What is prompt injection and how can it be prevented?

Prompt injection is an attack where malicious instructions are disguised as legitimate input, tricking an LLM into following them rather than treating them as content to process. Mitigation requires defence in depth: input validation, strict separation between instructions and data where possible, output monitoring, and limiting what actions a model can take autonomously.

Are standard cybersecurity tools enough to protect LLMs?

No. Firewalls, endpoint protection and network monitoring remain necessary but weren't designed to catch LLM-specific risks like prompt injection or training data poisoning. Protecting LLMs requires specialised, AI-focused controls alongside your existing security stack.

How often should LLM security assessments be conducted?

Ideally quarterly or biannually, and always after significant model updates, new integrations, or the emergence of new threats, so your security posture keeps pace with how quickly the risk landscape changes.

How can UK businesses stay GDPR- and EU AI Act-compliant when using LLMs?

By adhering to UK GDPR's data protection principles, running data protection impact assessments for high-risk AI use, documenting how AI is used across the organisation, and tracking the EU AI Act's phased deadlines (Article 50 transparency duties from August 2026, and high-risk system obligations from December 2027 for most Annex III systems).