Vulnerability Scanner

A Vulnerability Scanner in the context of Large Language Models (LLMs) serves as a crucial tool to identify and mitigate potential weaknesses that could be exploited to compromise the model's integrity or to generate harmful, misleading, or inappropriate content. By simulating various attack vectors, including prompt injection, adversarial inputs, and data extraction attempts, this evaluation component helps in preemptively identifying areas where the model might act unpredictably or against its ethical guidelines. The goal is to reinforce the model's defenses, ensuring it remains robust against manipulation and misuse while maintaining user trust and safety.

• Prompt Injection Resistance: Tests the model's ability to handle malicious inputs designed to exploit vulnerabilities, ensuring it doesn't produce unintended or harmful outputs.

• Adversarial Attack Robustness: Evaluates the model's resilience against inputs crafted to deceive or mislead it, maintaining integrity under adversarial conditions.

• Data Privacy and Leakage Prevention: Assesses the model's capability to safeguard sensitive information, preventing unintended disclosure of private or training data.

• General Security Vulnerabilities: Identifies other potential security flaws that could be exploited, ensuring comprehensive protection against a wide range of threats.

Go through individual implementation with examples to understand a suite of use cases covered under the Vulnerability Scanner Category