OWASP Top 10 for LLMs: The New AI Security Rulebook
Large Language Models (LLMs) are transforming how we build, scale, and interact with technology. But with this new wave of innovation comes a new class of cyberthreats — ones that aren’t written in code, but in words.
As LLMs become deeply embedded in apps, workflows, and autonomous agents, security teams must understand the vulnerabilities unique to GenAI systems. The OWASP Top 10 for LLMs brings much-needed clarity to these risks.
1. Prompt Injection - attackers overwrite your system rules with crafted text.
2. Jailbreaks - force the LLM to ignore guardrails and output unrestricted content.
3. Training Poisoning - malicious data creates hidden triggers + backdoors.
4. Model Theft - extraction or cloning of your model weights.
5. Data Leaks - unintended exposure of private or internal info.
6. Insecure Tool Use - LLM-agents triggering unsafe APIs or actions.
7. Overreliance - treating LLM output as truth without validation.
8. DoS Token Attacks - long or recursive prompts that drain compute.
9. Supply-Chain Risks - compromised datasets, plugins, models, or vector DBs.
10. Agent Misalignment - autonomous agents taking unsafe or unintended actions.
AI isn't just a model. It's a security attack surface.
As LLMs move from experimentation to production, security needs to move from optional to foundational.
Before you innovate, automate, or scale, secure your LLM stack. Harden first — then innovate.
