What is the #1 LLM security risk?

Prompt Injection is the #1 risk in the OWASP Top 10 for LLM Applications. It occurs when user input manipulates the AI to ignore its instructions, and it affects virtually every LLM-powered application.

OWASP Top 10 for LLMs (2025)

The OWASP Top 10 for LLM Applications (2025): 1. Prompt Injection, 2. Insecure Output Handling, 3. Training Data Poisoning, 4. Model Denial of Service, 5. Supply Chain Vulnerabilities, 6. Sensitive Information Disclosure, 7. Insecure Plugin Design, 8. Excessive Agency, 9. Overreliance, 10. Model Theft.

1. Prompt Injection

User input manipulates the AI to ignore its instructions. This is the most widespread LLM vulnerability and affects virtually every application that passes user input to a model. Learn more about prompt injection.

2. Insecure Output Handling

LLM output is used in downstream systems without sanitization. If a model's output is rendered as HTML, inserted into SQL queries, or passed to system commands, attackers can use prompt injection to achieve XSS, SQLi, or command injection through the AI.

3. Training Data Poisoning

Malicious data is introduced during training or fine-tuning, causing the model to produce biased, incorrect, or harmful outputs. This is particularly concerning for models fine-tuned on user-submitted data.

4. Model Denial of Service

Attackers craft inputs that consume excessive compute resources, causing service degradation or outages. Long context inputs, recursive prompts, and resource-intensive generation requests are common vectors.

5. Supply Chain Vulnerabilities

Risks from third-party models, training data, plugins, and libraries. Using untrusted model weights, poisoned training datasets, or vulnerable dependencies can compromise the entire application.

6. Sensitive Information Disclosure

The model reveals confidential data from its training data, system prompt, or connected data sources. This includes prompt leaking, PII exposure, and unintended memorization of training data.

7. Insecure Plugin Design

LLM plugins and tool integrations lack proper access controls, input validation, or sandboxing. A compromised plugin can give attackers access to external systems, databases, or APIs.

8. Excessive Agency

The AI system has more permissions, tools, or autonomy than necessary. When combined with prompt injection, excessive agency allows attackers to perform unauthorized actions through the AI.

9. Overreliance

Systems or users trust LLM outputs without verification. AI hallucinations, factual errors, and manipulated outputs can lead to incorrect decisions when there's no human review or fact-checking layer.

10. Model Theft

Unauthorized extraction of model weights, architecture, or training data through API queries, side-channel attacks, or insider access. Stolen models can be used to find vulnerabilities or replicate proprietary capabilities.

How LochBot Helps

LochBot's scanner primarily addresses risk #1 (Prompt Injection) and #6 (Sensitive Information Disclosure) by testing your system prompt against 31 known attack patterns. Try it free.