AI Agent Safety Testing
Description
AI agent safety testing evaluates autonomous AI agents that interact with external tools, APIs, and systems to ensure they operate safely and as intended. This technique assesses whether agents correctly understand tool specifications, respect usage constraints, handle errors gracefully, avoid unintended side effects, and make safe decisions during multi-step reasoning. Testing includes boundary condition validation (extreme parameters, edge cases), permission verification (ensuring agents don't exceed authorized actions), and cascade failure analysis (how tool errors propagate through agent workflows).
Example Use Cases
Safety
Testing an AI agent with database access to ensure it only executes safe read queries and cannot be manipulated into running destructive operations like deletions or schema modifications.
Security
Testing a healthcare AI agent with electronic health record access to ensure it correctly interprets permission levels, cannot be prompted to access unauthorised patient data, and maintains audit logs of all record queries.
Verifying that a customer service agent with CRM and payment processing tools cannot be manipulated through adversarial prompts to refund transactions outside policy boundaries or expose customer financial information.
Reliability
Ensuring an educational AI assistant with gradebook access reliably validates student identity, cannot be socially engineered into changing grades, and handles grade calculation edge cases without data corruption.
Limitations
- Comprehensive testing of all possible tool interactions, parameter combinations, and prompt variations is infeasible for agents with access to many tools, requiring risk-based prioritisation of test scenarios.
- Agents may exhibit unexpected emergent behaviors when composing multiple tools in novel ways not anticipated during testing.
- Difficult to test for all possible security vulnerabilities, especially when tools themselves may have undiscovered vulnerabilities.
- Testing in sandboxed environments may not capture all real-world failure modes and integration issues.
- Requires specialised expertise in both LLM security (prompt injection, jailbreaking) and domain-specific safety considerations, which may not exist within a single team.
- As underlying LLMs and available tools evolve, previously safe agent behaviors may become unsafe, necessitating continuous re-evaluation rather than one-time testing.
- Creating realistic adversarial test cases that anticipate how malicious users might manipulate agents requires red-teaming skills and understanding of social engineering tactics.