OWASP LLM Top 10: FAIR

# OWASP Top 10 for LLMs Mapped to FAIR Risk Assessment Framework

This document outlines an approach to evaluate the [OWASP Top 10 for Large Language Model Applications](https://owasp.org/www-project-top-10-for-large-language-model-applications/#) using the [Factor Analysis of Information Risk (FAIR)](https://www.fairinstitute.org/what-is-fair) framework. The goal is to quantify the risk associated with each vulnerability in terms of its impact on the confidentiality, integrity, and availability of information assets.

## General Approach to Applying FAIR

For each LLM vulnerability, the following FAIR components can be analyzed:

- **Threat Event Frequency (TEF)**: Estimate how often a threat event exploiting the vulnerability might occur.
- **Vulnerability (VULN)**: Assess the likelihood that the threat event will successfully exploit the vulnerability.
- **Contact Frequency (COF)**: Determine the frequency with which threat agents come into contact with the vulnerability.
- **Probability of Action (POA)**: Estimate the likelihood that the threat agent will act on the vulnerability.
- **Loss Magnitude (LM)**: Estimate the potential impact or loss magnitude resulting from a successful exploit.

## LLM01: Prompt Injection

- **TEF**: High in environments where user inputs are frequently processed.
- **VULN**: Moderate to high, depending on input validation measures.
- **COF**: High in interactive systems accessible by numerous users.
- **POA**: High, given the low cost and potential impact of exploitation.
- **LM**: Can vary from low to high, based on the sensitivity of the manipulated outputs.

## LLM02: Insecure Output Handling

- **TEF**: Moderate, depending on the application's output handling mechanisms.
- **VULN**: High if outputs are not properly sanitized or encoded.
- **COF**: Moderate in systems where outputs are dynamically generated based on user inputs.
- **POA**: Moderate, as exploiting these vulnerabilities requires specific knowledge.
- **LM**: Varies, potentially high if leading to unauthorized actions or data exposure.

## LLM03: Training Data Poisoning

- **TEF**: Low to moderate, as it requires access to the training data pipeline.
- **VULN**: High, since poisoning can significantly impact model behavior.
- **COF**: Low, given the controlled environments of training data collection and processing.
- **POA**: Low to moderate, dependent on the attacker's motivation and resources.
- **LM**: High, due to the potential for widespread impact on model decisions.

## LLM04: Model Denial of Service

- **TEF**: Moderate, especially in publicly accessible models.
- **VULN**: Moderate to high, based on resource management and input validation.
- **COF**: Moderate to high for internet-facing models.
- **POA**: High, given the potential disruption and relatively low effort required.
- **LM**: High, due to operational disruption and potential recovery costs.

## LLM05: Supply-Chain Vulnerabilities

- **TEF**: Low to moderate, depending on the security of the supply chain.
- **VULN**: High, as compromise in the supply chain can have widespread effects.
- **COF**: Low, requiring specific targeting or insider access.
- **POA**: Moderate, influenced by the attractiveness of the target and the attacker's capabilities.
- **LM**: High, given the potential for systemic weaknesses and widespread impact.

## LLM06: Sensitive Information Disclosure

- **TEF**: Moderate, especially in systems handling sensitive data.
- **VULN**: High, if data protection measures are inadequate.
- **COF**: Moderate to high, depending on system accessibility.
- **POA**: High, due to the value of sensitive information.
- **LM**: High, considering the potential for financial, reputational, and legal impacts.

## LLM07: Insecure Plugin Design

- **TEF**: Low to moderate, based on the plugin ecosystem's security practices.
- **VULN**: High for systems heavily reliant on plugins.
- **COF**: Low, requiring specific knowledge and access to exploit.
- **POA**: Moderate, contingent upon the perceived value of exploitation.
- **LM**: Varies, can be high if leading to system compromise or data breaches.

## LLM08: Excessive Agency

- **TEF**: Low, as it requires specific conditions and knowledge to exploit.
- **VULN**: Moderate, dependent on the implementation of decision-making constraints.
- **COF**: Low, limited to scenarios where the model has significant control.
- **POA**: Low to moderate, based on the complexity of exploiting such vulnerabilities.
- **LM**: Moderate to high, due to potential unintended actions or decisions.

## LLM09: Overreliance

- **TEF**: High, as overreliance is a common issue in technology adoption.
- **VULN**: Not applicable, as overreliance is more about user behavior than a technical vulnerability.
- **COF**: High, inherent in the deployment of LLM technologies.
- **POA**: Not applicable.
- **LM**: Moderate to high, due to potential operational and strategic risks.

## LLM10: Model Theft

- **TEF**: Low to moderate, depending on access controls and the value of the model.
- **VULN**: High for valuable models without adequate protection.
- **COF**: Low, requiring targeted efforts to access and exfiltrate the model.
- **POA**: Moderate, driven by the potential gains from stealing the model.
- **LM**: High, due to intellectual property loss and competitive disadvantage.

Note: The FAIR analysis for each vulnerability is a high-level estimation intended to guide risk assessment efforts. Organizations should perform detailed FAIR analyses based on their specific contexts, assets, and threat landscapes.