OWASP LLM Top 10⏱ 5 min overview

OWASP LLM Top 10 (2025)

The 10 most critical security risks in large language models, as defined by OWASP — each explained for business leaders.

OWASP — the Open Worldwide Application Security Project — is a globally respected nonprofit that sets the standard for application security. For decades, their "Top 10" lists have shaped how organizations prioritize security investments. In 2023, they released their first Top 10 specifically for Large Language Models, updated in 2025 to reflect how rapidly the threat landscape has evolved. This list is not theoretical — it is built from real-world incidents reported by security researchers, enterprise teams, and penetration testers working with AI systems in production.

Why does this list matter to you as a business leader? Because AI is no longer a pilot project — it is infrastructure. Every LLM your organization deploys, connects to internal data, or exposes to customers carries risk. The OWASP LLM Top 10 gives you a shared vocabulary with your security team, a prioritization framework for AI risk investment, and a checklist against which to evaluate any AI vendor or internal deployment. The ten risks below range from attackers hijacking your AI's behavior to your AI leaking confidential data to runaway costs from uncontrolled usage. Each card links to a full explanation with real-world examples.

LLM01:2025
Prompt Injection
Attackers embed malicious instructions in content the AI reads, hijacking its behavior without touching your systems.
🔴 Critical
LLM02:2025
Sensitive Information Disclosure
LLMs can leak confidential data from their training, from live conversations, or from connected internal systems.
🟠 High
LLM03:2025
Supply Chain Vulnerabilities
The AI models, plugins, and data your teams plug in may carry hidden risks from their own compromised supply chains.
🟠 High
LLM04:2025
Data and Model Poisoning
Attackers corrupt the data the AI learns from, permanently manipulating its behavior in ways that are very hard to detect.
🔴 Critical
LLM05:2025
Improper Output Handling
When AI-generated content is used without validation, it can inject malicious code into websites, databases, and downstream systems.
🟠 High
LLM06:2025
Excessive Agency
AI agents given too many permissions cause catastrophic damage when compromised — turning any vulnerability into a critical incident.
🔴 Critical
LLM07:2025
System Prompt Leakage
The hidden instructions that control your AI's behavior can be extracted by attackers, exposing confidential business logic.
🟡 Medium
LLM08:2025
Vector and Embedding Weaknesses
The AI memory systems that power knowledge retrieval introduce new attack surfaces and data exposure risks.
🟠 High
LLM09:2025
Misinformation
AI systems confidently generate false information — and organizations act on it, creating legal, financial, and reputational risk.
🟡 Medium
LLM10:2025
Unbounded Consumption
Without rate limits and cost controls, AI systems can be exploited to exhaust budgets and deny service to legitimate users.
🟡 Medium
💡

Click any card to explore the full explanation with interactive examples, real-world scenarios, and practical defenses — all written for business leaders, not engineers.