Introduction
Imagine giving an AI assistant access to your company’s customer records, internal documents, emails and business tools. It can save hours of work and make employees more productive. But what happens if someone tricks that AI into revealing confidential information or convinces it to take an action it was never supposed to take?
This is where LLM security becomes critical.
Large Language Models (LLMs) are quickly moving from experimental chatbots to systems used in customer service, software development, research, finance and everyday business operations. As adoption grows, so does the attack surface.
The OWASP Top 10 for LLMs 2026 provides a practical way for organizations to understand the most important security risks associated with applications powered by large language models. The latest edition reflects updated rankings, broader threat coverage and research based on real-world AI security incidents.
What Is the OWASP Top 10 for LLMs?
The OWASP Top 10 for LLMs is a security framework developed by the OWASP GenAI Security Project to highlight critical vulnerabilities affecting LLM-powered applications. Think of it as a security checklist for the AI era. The 2026 edition covers risks ranging from manipulated prompts and sensitive data exposure to excessive AI autonomy, poisoned data and uncontrolled resource consumption. Here are the ten risks businesses should know about.
1. Prompt Injection
A prompt injection happens when someone gives an AI instructions designed to override its intended behavior.
For example, imagine a customer-service AI that is instructed to summarize customer complaints. An attacker could hide instructions inside a document the AI reads, telling it to ignore its original task and reveal internal information.
The important point is simple: AI can be influenced by information it processes, not just by what users directly type.
2. Sensitive Information Disclosure
AI applications often work with valuable information customer details, financial records, internal documents or proprietary business information. If security controls are weak, an LLM may expose information that a user was never authorized to access. For a business, the consequences can go far beyond a technical problem. Data exposure can damage customer trust, create compliance concerns and affect a company’s reputation.
3. Excessive Agency
This is one of the biggest concerns as AI systems become more capable of taking action rather than simply generating answers. Imagine an AI assistant that can read emails, create orders and issue refunds. If it has more permissions than necessary, a manipulated or incorrect AI decision could result in a real financial loss.
The lesson is straightforward: an AI system should have only the access and authority it genuinely needs.
4. Supply Chain
Modern AI applications rarely rely on a single component. They may use third-party models, libraries, datasets, plugins, APIs and other external services. A weakness in any of these components can become a weakness in the final application. It is similar to building a house with materials from several suppliers: even if your construction is strong, a compromised material can still create a problem.
5. Data and Model Poisoning
AI systems depend heavily on data. If attackers manipulate training, fine-tuning or other data used by an AI system, they may influence how the model behaves. Consider an AI trained to identify fraudulent transactions. If its underlying data is deliberately manipulated, the system could gradually learn the wrong patterns. For organizations investing heavily in AI, protecting the data behind the model is just as important as protecting the model itself.
6. Unbounded Consumption
AI systems can consume significant computing resources. Without appropriate controls, excessive or malicious usage can increase costs, slow services or even make an application unavailable.
For example, an attacker repeatedly sending expensive requests to a public-facing AI service could turn a useful application into an unexpected financial burden.
AI security therefore isn’t only about preventing data breaches. Cost and resource management are security concerns too.
7. Misinformation
AI can produce information that sounds convincing but is simply wrong. Imagine an AI assistant helping an employee prepare a financial report. If an incorrect figure is presented confidently and nobody verifies it, the mistake can move from a chatbot conversation into an actual business decision. This is why organizations should not treat an AI-generated answer as automatically trustworthy. Accuracy checks and human oversight still matter.
8. Hidden Context Exposure
LLM applications often contain hidden instructions, context and other information that users are not supposed to see. Attackers may attempt to manipulate an application into revealing this hidden context. The risk becomes greater when that context contains sensitive business information, internal instructions or details that could help an attacker understand how the application operates.
9. Vector and Embedding Weaknesses
Many modern AI applications use technologies such as Retrieval-Augmented Generation (RAG) to retrieve information from company databases or document collections. This creates another security layer to protect. If the underlying documents, vectors or retrieval process are compromised, an attacker may influence what information the AI retrieves or what it ultimately reveals.
10. Improper Output Handling
The security problem does not necessarily end when an AI generates an answer. That output may be passed to another application, database or business process. If the output is trusted without appropriate validation, attackers may be able to exploit the system further.
In simple terms: AI-generated output should be treated carefully, especially when another system is going to act on it.
Why the OWASP LLM Top 10 Matters for Businesses?
The biggest mistake organizations can make is viewing AI security as something to think about after deployment. AI is becoming part of business operations, and security needs to be considered from the beginning. The 2026 OWASP list also reflects an important shift: AI is increasingly moving from systems that simply generate information to systems that can take actions. That makes permissions, monitoring, testing and security controls even more important.
How Greyhound Can Help?
At Greyhound, we believe that adopting AI should not mean compromising security. Organizations need visibility into how their AI applications behave, where vulnerabilities exist and how those weaknesses could affect the wider business environment. Through security assessment, automated scanning, black-box penetration testing and evidence-based security evaluation, Greyhound helps organizations identify weaknesses before they become costly incidents. As AI continues to evolve, AI security will not be a one-time exercise—it will be an ongoing part of building trustworthy digital systems.
Conclusion
None of this means AI should be treated as too risky to use. It means AI deserves the same kind of scrutiny businesses already give to any other system handling sensitive data or making real decisions, no more, no less. Every one of these ten risks comes down to the same root idea: AI is only as trustworthy as the guardrails built around it. Get those guardrails right, and AI becomes a genuine advantage. Skip them, and the very thing meant to save time and effort becomes the weakest link in the business. The organizations that get ahead here aren’t the ones avoiding AI. They’re the ones asking the harder question early: not just “what can this AI do for us,” but “what could go wrong if it’s manipulated, and are we actually prepared for that?”
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