Enhancing LLM Security: A Comprehensive Checklist to Threat Mitigation
A Proactive Checklist for Securing Large Language Models Against Emerging Cyber Threats and Adversarial Attacks
Word Ahead
This is a visual alternative to the traditional approach to Large Language Model (LLM) security, combining acquired knowledge from leading research institutions and industry experts with our own hands-on experience.
We utilize this comprehensive pentesting checklist as part of our rigorous testing routine for LLM implementations to ensure maximum security and robustness. By following this guide, you can identify potential vulnerabilities, misconfigurations, and other security risks specific to your LLM implementation, and take proactive steps to mitigate them.
Checklist
- Threat #1: Prompt Injection Attacks
- Test for direct prompt injection by attempting to bypass system prompts
- Attempt indirect prompt injection by manipulating data sources that feed into the LLM
- Try to override or modify system instructions within user inputs
- Test for jailbreaking attempts to bypass ethical guidelines or content restrictions
- Threat #2: Authorization Bypass
- Attempt to access data or perform actions beyond the user's authorized scope
- Test if the LLM can be tricked into making unauthorized API calls
- Check if sensitive information can be extracted through carefully crafted prompts
- Verify if the LLM respects user roles and permissions in its responses
- Threat #3: Data Leakage
- Probe for potential exposure of training data through specific queries
- Test if personal or sensitive information can be extracted from the model
- Check for unintended disclosure of system architecture or backend details
- Attempt to retrieve information from LLM caches that should be access-controlled
- Threat #4: Input Validation and Sanitization
- Test for SQL injection in LLM-generated database queries
- Attempt XSS attacks through LLM-generated outputs
- Check for command injection possibilities in LLM-processed inputs
- Verify proper handling and escaping of special characters
- Threat #5: Vector Database Security
- Test access controls on vector database queries
- Attempt to bypass document-level security in vector stores
- Check for potential data leakage through similarity searches
- Verify proper synchronization of ACLs between source systems and vector databases
- Threat #6: API and External Service Interactions
- Test for unauthorized API calls through LLM-generated requests
- Attempt to manipulate API parameters to gain elevated privileges
- Check for potential confused deputy attacks in multi-system interactions
- Verify proper identity propagation in API calls made by the orchestrator
- Threat #7: LLM-Generated Code Execution
- Test sandbox escape attempts in environments running LLM-generated code
- Attempt to inject malicious code through crafted prompts
- Check for unauthorized library imports or function calls in generated code
- Verify resource usage limits and execution timeouts
- Threat #8: Memory and Context Manipulation
- Attempt to poison the LLM's short-term or long-term memory
- Test for context leakage between different user sessions
- Try to manipulate the context window to gain unauthorized information
- Check for proper clearing of sensitive data from the LLM's working memory
- Threat #9: Autonomous Agent Vulnerabilities
- Test for unauthorized actions in multi-agent systems
- Attempt to manipulate agent decision-making processes
- Check for potential data leakage between collaborating agents
- Verify proper access controls in agent-to-agent communications
- Threat #10: MLOps Pipeline Security
- Attempt to poison training data used for model fine-tuning
- Test for unauthorized access to model versioning and deployment systems
- Check for potential supply chain vulnerabilities in the ML pipeline
- Verify proper access controls on training logs and model artifacts
- Threat #11: Orchestrator Security
- Test for potential bypass of orchestrator-level authorization checks
- Attempt to manipulate identity information passed by the orchestrator
- Check for proper handling of errors and edge cases in the orchestration layer
- Verify secure implementation of any caching mechanisms in the orchestrator
- Threat #12: Output Validation and Filtering
- Test if malicious or sensitive content can bypass output filters
- Attempt to trick the system into generating harmful or inappropriate responses
- Check for potential data leakage through carefully crafted output requests
- Verify proper handling of PII and other sensitive information in LLM outputs
Common AI Applications for Testing
Syn Cubes' AI/LLM penetration testing is adaptable to any implementation or use case. Whether you are integrating a chatbot into your web applications, utilizing generative AI to enhance customer journeys, or deploying internal tools to improve operational efficiency, these LLMs often share similar vulnerabilities and cybersecurity risks. Syn Cubes' penetration testing services are designed to identify these issues effectively.
AI Vulnerability Detection and Remediation Made Easy
The Syn Cubes platform connects you with top-tier global offensive security experts. By adopting this approach, you can tap into a vast and diverse pool of perspectives and expertise, gaining real-time insights and clear visibility into the team testing activities. For AI and LLMs, Syn Cubes operators follow a meticulously crafted testing playbook that delivers top-tier proof of concepts and guides you through the remediation phase. They can take it to the next level by providing you and your team with hands-on remediation support.
Expected Outcomes from Syn Cubes AI/LLM Penetration Testing
Testing reports from the Syn Cubes team are delivered in real-time via the Syn Cubes Helios Platform. This allows you to gain insights into the testing coverage of your current LLM implementation(s). All reports and identified vulnerabilities are reviewed by an internal team to ensure you receive only impactfull actionable results.