LLM Pentesting Checklist and Tools

LLM Penetration Testing Checklist

When engaging in penetration testing for Large Language Models (LLMs), it's essential to have a structured checklist to ensure comprehensive coverage of potential vulnerabilities. Here’s a detailed checklist based on best practices and security frameworks like OWASP.

1. Threat Modeling

• Identify potential attack vectors specific to LLMs (e.g., prompt injections, data leakage).

• Assess the impact of various threats, including adversarial attacks and model poisoning.

2. Data Handling

• Ensure proper sanitization of input data to prevent injection attacks.

• Implement strict access controls for sensitive training data.

3. Model Evaluation

• Regularly test the model against known vulnerabilities and adversarial examples.

• Use automated tools to evaluate model robustness against prompt injections.

4. Security Audits

• Conduct regular audits of the model's architecture and deployment environment.

• Evaluate third-party integrations for security compliance.

5. Monitoring and Logging

• Implement logging mechanisms to track interactions with the LLM.

• Monitor for unusual patterns that may indicate an attack.

6. Incident Response Plan

• Develop a response plan for potential breaches or security incidents.

• Include procedures for containment, eradication, and recovery.

7. User Input Validation

• Validate and sanitize all user inputs before processing.

• Implement rate limiting to prevent abuse through repeated requests.

8. Access Control

• Enforce strict authentication and authorization protocols for users accessing the LLM.

• Regularly review permissions and access logs.

9. Model Updates and Patching

• Keep the model updated with the latest security patches.

• Regularly retrain the model with updated datasets to mitigate new threats.

10. Compliance and Regulations

• Ensure adherence to relevant regulations (e.g., GDPR, CCPA) regarding data privacy and security.

• Document compliance measures taken during development and deployment.

Top 9 Tools for LLM Security Testing

Here are ten essential tools that can assist in the penetration testing of LLMs:

1. Plexiglass

Plexiglass is a command-line interface (CLI) toolkit designed to detect and protect against vulnerabilities in LLMs. It allows users to test LLMs against various adversarial attacks, such as prompt injections and jailbreaking. The toolkit also benchmarks security, bias, and toxicity by scraping adversarial prompts from various sources.

• Key Features:

  • Modes for conversational analysis (llm-chat) and vulnerability scanning (llm-scan).

  • Metrics evaluation for toxicity and personally identifiable information (PII) detection.

  • Continuous updates with new adversarial prompt templates.

• GitHub Stars: 120+ stars

2. PurpleLlama

PurpleLlama is an open-source suite developed by Meta that focuses on enhancing the security of LLMs through various tools for input moderation, threat detection, and evaluation benchmarks. It includes components like Llama Guard and Code Shield to help mitigate risks associated with LLMs.

• Key Features:

  • Input moderation to filter potentially harmful prompts.

  • Security benchmarks to evaluate model robustness.

  • Community-driven development with active engagement in issues and pull requests.

• GitHub Stars: 2700+ stars

3. Garak

Garak is a vulnerability scanner specifically designed for chatbots and other LLM applications. It probes for various weaknesses, including hallucinations, data leaks, and prompt injections, providing detailed security reports on the model's performance.

• Key Features:

  • Comprehensive scanning for multiple types of vulnerabilities.

  • Detailed reporting on strengths and weaknesses.

  • Open-source accessibility for broader community use.

• GitHub Stars: 1400+ stars

4. Rebuff

Rebuff is a multi-layered defense tool aimed at protecting AI applications from prompt injection attacks. It employs heuristics to filter malicious input, uses a dedicated LLM for analysis, stores embeddings of previous attacks in a vector database, and implements canary tokens to detect data leakages.

• Key Features:

  • Heuristic filtering of potentially dangerous prompts.

  • Detection of injection attempts using an LLM-based approach.

  • Vector database for recognizing past attack patterns.

• GitHub Stars: 1100+ stars

5. LLMFuzzer

LLMFuzzer is an open-source fuzzing framework tailored for testing LLMs through their API integrations. It aims to streamline the process of discovering vulnerabilities by simulating various attack scenarios against LLM APIs.

• Key Features:

  • Modular architecture allowing easy extension and customization.

  • Support for multiple fuzzing strategies.

  • Integration testing capabilities for LLM APIs.

• GitHub Stars: 225+ stars

6. Vigil

Vigil is a Python library and REST API designed to assess prompts and responses from LLMs, focusing primarily on detecting prompt injections and jailbreak attempts. It analyzes user inputs and model outputs to identify potential threats, helping developers secure their models against adversarial attacks.

• Key Features:

  • Detection of prompt injections and jailbreak attempts.

  • REST API for easy integration into existing applications.

  • Ongoing development with a focus on enhancing detection capabilities.

• GitHub Stars: 300+ stars

7. Whistleblower

Whistleblower is a tool designed to infer the system prompt of an AI agent based on its generated text outputs. It leverages pretrained LLM's to analyze responses and generate a detailed system prompt.

• Key Features:

  • Leaking system prompts and capability discovery of any API accessible LLM App.

  • Provides a CLI interface.

• GitHub Stars: 100+ stars

8. Adversarial Robustness Toolbox (ART)

The Adversarial Robustness Toolbox (ART) is a Python library that provides adversarial training tools to help LLMs recognize and defend against various types of attacks, including evasion, poisoning, extraction, and inference attacks. It includes defense modules to harden AI applications against adversarial threats.

• Key Features:

  • Comprehensive support for adversarial training techniques.

  • Multiple robustness metrics and certification options.

  • Integration capabilities with other security tools like Microsoft Counterfit.

• GitHub Stars: 4800+ stars

9. Microsoft Counterfit

Microsoft Counterfit is a command-line interface tool designed to orchestrate adversarial training attacks against AI models. It provides an automation layer for assessing the security of machine learning models by integrating with various adversarial training frameworks like ART.

• Key Features:

  • Preloaded with attack algorithms for testing AI systems.

  • Logging capabilities for telemetry information related to model failures.

  • Compatibility with on-premises, cloud, and edge AI models.

• GitHub Stars: 800+ stars

By employing this checklist along with these tools, organizations can significantly enhance their security posture when working with Large Language Models, ensuring they are resilient against emerging threats in the AI landscape.