OWASP Top 10 LLM Security Risks
The OWASP Top 10 LLM Security Risks is a comprehensive awareness document for developers and security teams working with Large Language Models. This guide focuses on the most critical vulnerabilities and security risks associated with LLM applications.
2024 Impact Statistics
85%
Organizations Affected
Reported LLM security incidents42%
Data Leakage
From prompt injection attacks63%
Model Misuse
Unauthorized access incidents78%
Security Gaps
In LLM implementationsPrompt injection occurs when an attacker manipulates the LLM's input to make it ignore its original instructions and perform unintended actions. This can lead to:
- Data leakage
- Unauthorized access
- Malicious code execution
- System compromise
Failure to properly validate and sanitize LLM outputs can lead to various security issues:
- Cross-site scripting (XSS)
- Remote code execution
- Data injection attacks
- System compromise
Training data poisoning involves manipulating the data used to train LLMs, which can result in:
- Biased outputs
- Malicious behavior
- Data leakage
- Model manipulation
Denial of Service attacks on LLMs can be achieved through:
- Resource exhaustion
- Input flooding
- Complex prompt manipulation
- System overload
Supply chain vulnerabilities in LLM ecosystems include:
- Compromised model weights
- Malicious dependencies
- Untrusted data sources
- Insecure model distribution
LLMs may inadvertently disclose sensitive information through:
- Training data leakage
- Prompt manipulation
- Model outputs
- System responses
Insecure plugin design can lead to:
- Unauthorized access
- Data leakage
- System compromise
- Plugin abuse
Excessive agency in LLMs can result in:
- Unauthorized actions
- System manipulation
- Resource abuse
- Security breaches
Overreliance on LLM outputs can lead to:
- Incorrect decisions
- Security vulnerabilities
- System failures
- Data integrity issues
Model theft can occur through:
- Unauthorized access
- Model extraction
- Weight stealing
- Architecture copying
| Rank | Vulnerability | Description | Impact |
|---|---|---|---|
| 1 | Prompt Injection | Manipulation of LLM inputs to bypass security controls and perform unintended actions | Critical |
| 2 | Insecure Output Handling | Failure to properly validate and sanitize LLM outputs leading to various attacks | Critical |
| 3 | Training Data Poisoning | Manipulation of training data to introduce biases or malicious behavior | High |
| 4 | Model Denial of Service | Attacks that exhaust resources or overload the LLM system | High |
| 5 | Supply Chain Vulnerabilities | Security risks in the LLM development and deployment pipeline | High |
| 6 | Sensitive Information Disclosure | Inadvertent leakage of sensitive data through LLM interactions | Critical |
| 7 | Insecure Plugin Design | Vulnerabilities in LLM plugin architecture and implementation | High |
| 8 | Excessive Agency | LLMs performing actions beyond their intended scope | High |
| 9 | Overreliance | Uncritical trust in LLM outputs leading to security issues | Medium |
| 10 | Model Theft | Unauthorized access and copying of LLM models and weights | High |
Based on the OWASP Top 10 LLM Security Risks, organizations must implement robust security measures and regular audits to protect their LLM applications. Proper security controls, monitoring, and incident response procedures are essential for mitigating these risks.