Large Language Model (LLM) integration risks for SaaS and enterprise

February 17, 2026

Adam King
Director

The rapid adoption of Large Language Models (LLMs) is transforming how SaaS platforms and enterprise applications operate. From embedded copilots and automated support agents to internal knowledge-base search and workflow automation, organisations are increasingly integrating LLM APIs into existing services to deliver faster and more intuitive user experiences.
Nevertheless, as adoption accelerates, so too does the emergence of LLM security vulnerabilities, a rapidly-evolving attack vector we cannot yet fully understand. In many cases, integrations are being deployed into production environments faster than security models and assurance processes can adapt. For attackers, this presents a new and expanding attack surface, particularly where LLMs interact with sensitive data, internal systems, and business logic.
The LLM integration risk isn’t really the models being used. It’s the integration layer, where user input, application context, and AI-generated outputs create new challenges for security. As SaaS vendors and internal development teams embed LLM functionality into customer-facing and operational systems, the need to understand LLM integration security is becoming increasingly important.
LLM security risks emerge in the integration layer
Most organisations consume LLM capabilities via API rather than building models from scratched . These integrations typically connect the model to internal data sources, customer interfaces, or other backend services to provide more relevant and useful responses. Common examples include chat-based support assistants, document summarisation tools, and AI-enhanced productivity features.
To function effectively, the model is often given contextual access. This might include user prompts, system instructions, proprietary data, or internal documentation. While this improves accuracy and usability of AI integrations, it also introduces a new trust boundary where external AI providers can access, and sometimes change, sensitive data.
Historically, user input was constrained and validated before interacting with backend systems. With LLM-driven interfaces, inputs are deliberately open-ended and conversational. This flexibility is a core strength of the technology, but it also creates new LLM security risks that traditional application security thinking sought to avoid.
From a SaaS AI security perspective, this integration layer is where exposure tends to be of highest concern. It is the point at which natural language and application logic converge, and where many security professionals do not yet fully trust the effectiveness of guardrails.
Prompt injection attacks as a new entry point
One of the most widely documented threats in LLM application security is the rise of prompt injection attacks. These attacks involve crafting input designed to manipulate the model’s behaviour, override instructions, or extract unintended information.
Unlike traditional injection techniques that target code execution, prompt injection attacks target the model’s interpretation layer. By structuring input in specific ways, an attacker may be able to influence how the model prioritises instructions, handles context, or reveals information.
In environments where LLMs are connected to internal systems or data sources, this can create a pathway to data breaches. A malicious user may attempt to convince the model to ignore restrictions or reveal sensitive data by circumventing controls built into the model’s instructions.
As organisations continue securing LLM APIs and expanding their use cases, prompt injection remains one of the most persistent and difficult-to-detect threats. It is also a core focus area in modern AI application security testing, as new techniques are documented regularly.
Data exposure risks in LLM integrations
Another major category of LLM security vulnerabilities relates to how models access and process data. Many enterprise implementations allow models to retrieve information from internal data sources to provide more contextually accurate responses. While this significantly enhances usability, it also increases the risk of unintended data exposure.
Attackers may attempt to extract sensitive information by prompting the model to summarise internal documentation, expose hidden instructions, or retrieve contextual data. In SaaS environments, weaknesses in tenant isolation can create additional risk, particularly if the model has visibility across large, varied datasets.
Data exposure in LLM integrations may not always involve direct access to a database, but is sometimes caused by the gradual exposure of fragments of information through conversation. Over time, these fragments can be pieced together to reveal sensitive details about systems, customers, or operations.
From an enterprise AI security testing perspective, understanding what the model can see is a critical part of assessing real-world risks associated with LLM integrations.
LLM interactions with business logic can create new risks
Risk increases further when LLM integrations move beyond information retrieval and begin interacting with operational systems and processes. In some SaaS and enterprise environments, models are able to trigger actions such as querying internal services, generating tickets, or initiating workflows based on user prompts.
This effectively turns natural language into a command interface. If output validation is weak, or if application logic places too much trust in the model’s responses, attackers may attempt to manipulate behaviour through carefully structured prompts. This can cause interference with business processes which can be costly and time consuming to resolve. For example, a model might be persuaded to generate output that the application interprets as an authorised request.
This intersection between model output and system behaviour is now a key focus area in LLM integration security.
Mapping risks to the OWASP AI testing methodology
As LLM security vulnerabilities have become more widely understood, structured frameworks have emerged to help organisations assess and manage risk. The OWASP Top 10 for Large Language Model Applications provides a practical reference point, particularly for organisations deploying AI capabilities into production environments.
Using the OWASP framework, organisations can begin to think more systematically about AI application security testing. Traditional web and infrastructure testing remains essential, but substantial focus is now required at the model interaction layer, where behaviour can be influenced in non-traditional ways.
Specialist assessments such as AI penetration testing are designed to address these emerging risks. They focus on how models respond to adversarial prompts, how context is managed, and how outputs are interpreted by connected systems.
Why traditional testing may miss LLM-specific vulnerabilities
Most mature SaaS platforms and enterprise applications already undergo regular security testing. However, when LLM integrations are introduced, they create new entry points that may fall outside established testing methodologies.
An endpoint that accepts free-form natural language input behaves very differently from one that processes structured data. A system that allows a model to search and retrieve data contextually introduces different risks than a static data retrieval mechanism such as a database table. And an application that acts on model-generated output effectively extends the attack surface into the model’s decision-making and execution layer.
Without adapting testing approaches, these risks will stay hidden. LLM integration security requires a deeper understanding of how models interpret prompts, how they access data, and how their outputs influence application behaviour.
Targeted AI-focused assessments, including AI penetration testing, explore these boundaries. They methodically examine how susceptible a system is to prompt injection attacks, whether sensitive information can be extracted, and whether model outputs can be manipulated to influence system behaviour.
LLM integrations are a rapidly expanding attack surface
The speed at which organisations are adopting AI capabilities means that the LLM attack surface is growing quickly. New features are being introduced into SaaS platforms, internal tools, and customer-facing services, often as part of rapid innovation cycles.
While this brings significant operational value, it also increases the likelihood that LLM security vulnerabilities will emerge through design decisions, integration shortcuts, or insufficient guardrails. Attackers are already beginning to explore these environments, learning how models respond and where guardrails and system prompts can be exploited.
How can Sentrium help with AI security?
As organisations continue securing LLM APIs and expanding AI-enabled functionality, testing strategies must evolve alongside them. Structured assessments such as AI penetration testing help identify where integration design, data access, and model behaviour create unintended exposure, ensuring innovation does not outpace security.
If your organisation is evaluating AI security and are considering thorough testing against established frameworks, get in touch with our team.

*** This is a Security Bloggers Network syndicated blog from Cyber security insights & penetration testing advice authored by Adam King. Read the original post at: https://www.sentrium.co.uk/insights/large-language-model-llm-integration-risks-for-saas-and-enterprise

About Author

AndyC

Andy Curtis is an award-winning security consultant, researcher and public speaker. He has been working in the computer security industry since the early 1990s, having been employed by state and federal government, leading healthcare and banking providers across three continents. He has given talks about computer security for some of the world’s largest companies, worked with law enforcement agencies on investigations into hacking groups, and is a regular voice on TV and radio explaining IT security threats.

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