Core Concepts
Risks associated with Large Language Models (LLMs) require a comprehensive security assessment to mitigate potential threats effectively.
Abstract
This content delves into the risks posed by integrating Large Language Models (LLMs) across various sectors. It proposes a risk assessment process using tools like the OWASP risk rating methodology to identify and analyze potential threats for security practitioners, developers, and decision-makers working with LLM technology. The content outlines the methodology employed, scenario analysis, dependency mapping, impact analysis, and the creation of a threat matrix to provide stakeholders with actionable insights for effective risk mitigation strategies.
Structure:
- Introduction to LLMs in diverse sectors.
- Risks associated with LLMs.
- Existing studies by OWASP and MITRE.
- Proposed risk assessment process using OWASP methodology.
- Scenario analysis, dependency mapping, impact analysis.
- Creation of a threat matrix for stakeholders.
- Related work in the field of LLM security.
Stats
"The likelihood score is 6.75 for Prompt Injection."
"The likelihood score is 4.25 for Training Data Poisoning."
Quotes
"Despite significant efforts to align the models and implement defensive mechanisms to make LLMs more helpful and less harmful, attackers have found ways to circumvent these guardrails."
"Our outlined process serves as an actionable and comprehensive tool for security practitioners, offering insights for resource management and enhancing overall system security."