LLM Security
OWASP Top 10 for LLM Applications
OWASP Top 10 for Large Language Model applications, covering prompt injection, data poisoning, supply chain vulnerabilities, and model security risks.
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| Rank | Title | Summary | Details |
|---|---|---|---|
| #1 | Prompt Injection | Untrusted prompts can override system intent and manipulate tools or model behavior. | View Details |
| #2 | Insecure Output Handling | Treating model output as safe can lead to downstream injection and data integrity risks. | View Details |
| #3 | Training Data Poisoning | Compromised training or retrieval data can bias outputs and create hidden attack triggers. | View Details |
| #4 | Model Denial of Service | Unbounded prompts and request flooding can cause cost spikes and service degradation. | View Details |
| #5 | Supply Chain Vulnerabilities | Model and plugin dependencies can introduce compromised artifacts and unsafe behavior. | View Details |
| #6 | Sensitive Information Disclosure | Prompts, context windows, and outputs can leak secrets or regulated data. | View Details |
| #7 | Insecure Plugin Design | Poorly constrained plugins/tools allow privilege escalation and unsafe external actions. | View Details |
| #8 | Excessive Agency | Overly autonomous workflows amplify mistakes and abuse impact across connected systems. | View Details |
| #9 | Overreliance | Users and systems may trust inaccurate model outputs without verification safeguards. | View Details |
| #10 | Model Theft | Attackers can extract model behavior or assets through abuse of APIs and deployment gaps. | View Details |