OWASP's Top 10 Ways to Attack LLMs: AI Vulnerabilities Exposed

2026-03-07 · Source: IBM Technology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Intermediate, extended

Summary

The OWASP Top 10 for AI Large Language Models (LLMs) has been updated to reflect current threats encountered when deploying these models. This list, developed by a global nonprofit focused on practical security guidance, identifies the most common vulnerabilities. Key threats include Prompt Injection (ranked #1), where attackers bypass controls by manipulating input, and Sensitive Information Disclosure (#2), which has risen four spots due to data leakage from training data or model inversion attacks. Other critical vulnerabilities cover Supply Chain Vulnerabilities (#3) from unvetted open-source models and infrastructure, Data and Model Poisoning (#4) through compromised training data or RAG sources, and Improper Output Handling (#5) leading to downstream vulnerabilities like cross-site scripting. The list also addresses Excessive Agency, System Prompt Leakage, Vector Embedding Weaknesses, Misinformation, and Unbounded Consumption (Denial of Service/Denial of Wallet).

Key takeaway

For MLOps Engineers deploying LLMs, understanding the OWASP Top 10 is crucial for proactive defense. You should prioritize implementing robust input/output validation via AI firewalls, rigorously vetting all model and data sources, and establishing strong access controls across your LLM ecosystem to mitigate risks like prompt injection and sensitive data leakage. Regular penetration testing is essential to identify and address vulnerabilities before they are exploited.

Key insights

The OWASP Top 10 for LLMs highlights critical, evolving security threats from prompt injection to supply chain vulnerabilities.

Principles

• LLMs struggle to distinguish input from instructions.
• Unvetted open-source models pose significant supply chain risks.
• Small data errors can cascade into major model inaccuracies.

Method

Defenses include system prompt hardening, AI firewalls/gateways for input/output inspection, penetration testing, data sanitization, strong access controls, and vigilant configuration management.

In practice

• Implement an AI firewall between users and LLMs.
• Sanitize training data to prevent sensitive information disclosure.
• Vet all components in your LLM supply chain.

Topics

• LLM Security
• Prompt Injection
• Data Poisoning
• Supply Chain Vulnerabilities
• Model Inversion Attacks

Best for: AI Security Engineer, MLOps Engineer, AI Engineer

Related on AIssential

• Security Risks in Language Models (LLMs) — LLMs face unique security threats from manipulation, data leakage, and supply chain vulnerabilities across their lifecycle.
• Prompt Injection: The #1 AI Security Threat Every Developer Must Understand — Prompt injection is the top AI security threat, exploiting LLMs by overriding system instructions with malicious user input.
• The Developer’s Guide to LLM Security — LLM and agentic AI security introduces novel vulnerabilities requiring new architectural and testing approaches.
• Your On-Call Queue Is Hiding Your Most Critical System Design Flaws. — Agentic AI can precisely classify high-volume incidents, distinguishing critical system regressions from noise.
• Improving instruction hierarchy in frontier LLMs — Training LLMs with instruction hierarchy tasks improves safety steerability and prompt injection robustness.
• Guardrails for LLMs: How to Stop Your AI App From Saying Something Embarrassing in Production — LLMs, without external controls, can generate commercially damaging or embarrassing responses by answering helpfully but without business context.

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Editorial summary, takeaway, and curation by AIssential. Original article published by IBM Technology.