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AI Security

OWASP Top 10 for LLM Applications — Testing Chatbots, Copilots and RAG

The OWASP Top 10 for LLM Applications lists the most critical risks in software built on large language models — chatbots, copilots, retrieval-augmented generation (RAG) and AI features inside products.

Mutex Systems tests LLM applications against the list, from prompt injection and data leakage to over-privileged integrations, and helps you fix the design, not just the prompt.

Category
Testing Methodology
Jurisdiction
International
Issuing Body
OWASP GenAI Security Project
Current Version
2026 edition (released August 2026), succeeding the 2025 list
Who It's For
Teams shipping AI features to customers or staff — SaaS providers, banks using AI assistants, and organisations deploying internal copilots on company data.
Read the official source
What It Covers

Core Domains

Prompt injection (direct and indirect)
Sensitive information disclosure
Supply chain: models, datasets and plugins
Data and model poisoning
Improper output handling
Excessive agency of connected tools
System prompt leakage
Vector database and embedding weaknesses
Misinformation
Unbounded consumption and cost abuse
How It Works

A Practical Engagement Path

  1. 01

    Architecture review

    Map prompts, retrieval sources, tools and permissions.

  2. 02

    Adversarial testing

    Direct and indirect prompt injection, jailbreaks and data-exfiltration attempts.

  3. 03

    Integration testing

    Test what the model can reach — APIs, databases and actions.

  4. 04

    Guardrails

    Recommend design controls, filtering, least privilege and monitoring.

Our Approach

Fix the architecture, not just the prompt

Most LLM risks come from what the model is connected to. We limit what it can see and do so a successful injection causes little harm.

  • RAG and data-access review
  • Least-privilege tool design
  • Ongoing retests as models change
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Compliance, Continuously

LLM risks inside your AI governance

Findings are tracked in grComply against ISO 42001, NIST AI RMF and EU AI Act obligations, next to the risk register and AI system records.

  • AI systems and their LLM risks recorded together
  • Test findings mapped to ISO 42001 and EU AI Act controls
  • Retests recorded as evidence when models change
  • Human-oversight evidence for auditors

grComply is Mutex Systems' own multi-tenant GRC automation platform — the framework library, hybrid scanning, risk and audit workflow, and AI-assisted compliance behind every point above are live in production today.

See How grComply Works
FAQs

Common Questions About OWASP LLM Top 10

What is prompt injection?

An attacker places instructions in user input or in content the model reads — a web page, email or document — that override the developer’s instructions. It is the top LLM risk because it cannot be fully prevented by prompts alone.

How often should LLM applications be tested?

At launch and whenever the model, prompts, data sources or connected tools change, because each change can reopen old weaknesses.

How does this relate to the Agentic Top 10?

The LLM Top 10 covers applications built on language models; once a model plans and acts through tools and memory, the OWASP Top 10 for Agentic Applications covers the additional risks.

Let's Talk

Ready to Start Your OWASP LLM Top 10 Programme?

Send us your current posture and timeline. Within two working days you will receive a written response and a proposed scoping call.

No commitment requiredResponse within 2 working daysConfidential brief handling