As organisations rush to embed large language models into products, copilots and agents, they are exposing a new class of risk that traditional application security was never designed to catch. The OWASP Top 10 for LLM Applications is the industry’s reference for these risks, and every team shipping AI features should know it.
Why a dedicated list was needed
LLM applications fail in ways classic web security does not anticipate: they can be talked into ignoring instructions, coaxed into leaking data through their responses, or given too much autonomy over connected tools. The OWASP LLM Top 10 captures these distinctive failure modes so teams have a shared, structured way to reason about AI risk.
Prompt injection and data disclosure
Topping the list is prompt injection — manipulating the model into overriding its instructions, whether directly through user input or indirectly via content it reads. Close behind is sensitive information disclosure, where a model is coaxed into revealing data, secrets or other users’ information it should never expose.
Excessive agency and system-prompt leakage
As LLMs gain the ability to act through tools and agents, excessive agency becomes dangerous: an over-permissioned agent that is manipulated can take real, harmful actions. System-prompt leakage — extracting the hidden instructions and guardrails behind an assistant — gives attackers the blueprint to defeat those guardrails.
Supply chain, output handling and more
The list also covers risks in models, plugins and training data (supply chain and data poisoning), unsafe handling of model output that leads to classic flaws like XSS, weaknesses in vector and embedding stores behind retrieval systems, and unbounded consumption that drives cost and denial of service.
Testing against the framework
Knowing the risks is not enough; you have to test for them. Adversarial testing against each category — actively attempting prompt injection, data exfiltration and agent abuse — reveals what an attacker could really do to your AI features before you ship them, and again as they evolve.
How Attack Insights and DarkThreatX help
Attack Insights AI Security penetration tests your LLM applications against the OWASP Top 10 for LLMs, validating real exploitability rather than listing theoretical risks. DarkThreatX context helps prioritise by surfacing related leaked data and threat-actor interest, so AI risk is assessed in light of real-world activity.
Common mistakes to avoid
The common mistakes with the OWASP LLM Top 10 start with treating it as a checklist to read rather than a framework to test against; awareness without active testing changes nothing. Many teams secure the application around the model but ignore the model’s own behaviour, leaving prompt injection and data disclosure wide open. Some grant agents broad permissions for convenience, creating excessive-agency risk that a single manipulation can exploit. Others overlook the AI supply chain — models, plugins and training data — and the unsafe handling of model output that reintroduces classic flaws like XSS. And almost all underestimate how often these risks reappear as prompts and integrations change. Treating the framework as a continuous, adversarial testing programme is what actually reduces AI risk.
Key takeaways
- LLMs fail in ways classic appsec does not anticipate.
- Prompt injection and data disclosure lead the list.
- Excessive agency and system-prompt leakage are rising risks.
- Supply chain, output handling and consumption also feature.
- Test adversarially against each category before and after launch.
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