OWASP LLM Top 10: What Every Leader Should Know About AI Security
Ralf-Stiven Viru, CEOOWASP, the world's leading application security standard, released the updated LLM Top 10 framework for 2026. The most striking finding: prompt injection vulnerability is present in over 73% of AI deployments tested.
For any company that deployed ChatGPT or built an AI agent in the last year, this is a clear signal. AI security risks are not the same as traditional IT risks. Most companies don't yet have the controls in place.
What is OWASP LLM Top 10?
OWASP (Open Web Application Security Project) has defined the biggest web application security risks for the past two decades. Now the same approach has been extended to AI.
Over 600 security experts from 18 countries built a framework focused specifically on large language models (LLMs like ChatGPT, Claude, and Gemini). The 2026 update reflects the rapid rise of AI agents. Models increasingly make decisions and execute actions without human intervention.
The Ten Biggest AI Security Risks
| # | Risk | What it means in plain terms |
|---|---|---|
| 1 | Prompt injection | An attacker hides instructions in input that override the AI's rules |
| 2 | Insecure output handling | AI-generated code or content gets executed without verification |
| 3 | Training data poisoning | Attackers contaminate the data the model learned from |
| 4 | Model denial of service | Burning API budget or compute through overload |
| 5 | Supply chain risk | Third-party models, libraries, or datasets are compromised |
| 6 | Sensitive data disclosure | The model leaks info from training data or context |
| 7 | Insecure plugin design | Tools the AI uses lack proper authentication |
| 8 | Excessive agency | The AI has too many permissions and acts without human review |
| 9 | Overreliance | People trust AI outputs without verifying them |
| 10 | Model theft | Attackers copy your model via API queries |
Let's look more closely at the four risks that affect companies most directly.
1. Prompt Injection: The #1 Threat (73%)
LLMs cannot tell developer instructions apart from user input. Both look like natural text. That means anyone writing a message to the AI can hide commands that bypass its original rules.
Two forms exist:
- Direct injection: the user writes malicious input directly into the chat
- Indirect injection: malicious instructions are hidden in documents, emails, or web pages the AI reads
A real example: in one company's RAG system (a model that reads internal documents to answer questions), researchers hid instructions in a public document that caused the AI to leak confidential business intelligence and make API calls with elevated privileges.
What to do:
- Never let the AI take actions beyond the user's own permissions
- Insert a human approval step before sensitive operations
- Log and monitor unusual query patterns
2. Sensitive Data Disclosure: Your Employees Are the Biggest Risk
According to recent research, 48% of employees have uploaded sensitive company data into public AI tools. Samsung's famous 2023 incident was just the first of many. Someone pastes confidential code or customer data into ChatGPT to "get the work done faster."
Consequences:
- The model may memorize the information during future training
- Data flows to the provider's servers, where you have no control
- GDPR violation risk is real
What to do:
- A clear company AI policy on what can and cannot go into public tools
- Prefer enterprise versions (ChatGPT Enterprise, Claude for Work) where data isn't used for training
- Technical controls: DLP (Data Loss Prevention) solutions that detect sensitive data
3. Excessive Agency: Agents Doing More Than They Should
This risk has exploded with the rise of AI agents. The problem is simple: a developer gives the AI access to systems, but too broadly. The model can "read the database," but it actually has rights to delete, modify, and insert as well.
Three typical mistakes:
- Excessive functionality: the model uses tools that aren't needed for its task
- Excessive permissions: the API connects to the database with full rights when it only needs read access
- Excessive autonomy: the model executes financial transactions or sends emails without human approval
What to do:
- Principle of least privilege: the AI gets exactly as much access as its task requires
- Human approval before irreversible actions
- Every action logged and auditable
4. Overreliance: The Most Common Business Mistake
LLMs generate responses based on probabilities, not facts. But they do it with great confidence. Research shows nearly a third of AI responses contain inaccuracies or unverified claims.
This is especially dangerous in fields where the cost of mistakes is high:
- Law (made-up court rulings)
- Medicine (wrong diagnosis)
- Finance (misleading calculations)
- Hiring (biased recommendations)
Research shows that 74% of AI agents in production still require human oversight. The recommendation: 30–40% of development effort should go to evaluation and verification systems, not just building new features.
What Should You Do About It?
Most companies have rushed AI adoption and treated security as an afterthought. That's understandable. The market moves fast, competition pushes hard, employees demand new tools. But the consequences can be heavy: data leaks, GDPR fines, loss of trade secrets, reputational damage.
The good news: these risks are avoidable if a company does three things:
- Map where and how AI is used in the company. Often employees have already adopted tools that leadership doesn't know about
- Set clear rules: what data can go into public AI, what can't; which systems need enterprise-grade contracts
- Build controls: human approval for sensitive actions, logging, regular audits
These aren't complicated steps, but they need someone's attention. Just as IT security isn't "everyone's job," AI security can't be left on the side either.
If you want to know where AI risks are hiding in your company and how to mitigate them, get in touch.
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