Overview
LiteAgent tests web automation agents against dark patterns—deceptive UI designs that manipulate users. As AI agents automate more online tasks, they’re vulnerable to these same tricks that can cause unintended actions, exploited behaviors, and task failures.Key Features
- Multi-agent testing: Compare different agents using standardized test scenarios
- Performance metrics: Task Success Rate (TSR), Dark Pattern Susceptibility Rate (DPSR), and confusion matrices
- Data collection: SQLite logs, video recordings, HTML snapshots, and rrweb sessions
- Automated evaluation: Task validation, dark pattern detection, and statistical reporting
Architecture Overview
LiteAgent consists of three main components:- Collector
- Evaluation
- TrickyArena
The Collector module orchestrates agent execution:
- Manages different agent implementations
- Controls browser automation
- Records interactions and captures data
- Handles timeouts and error recovery
Supported Dark Patterns
LiteAgent tests against common deceptive patterns:- Bait and Switch - Promising one outcome, delivering another
- Disguised Ads - Ads masquerading as content
- Hidden Costs - Charges revealed only at checkout
- Roach Motel - Easy entry, difficult exit
- Sneak into Basket - Adding items without consent
Who Uses LiteAgent
Researchers
Study agent behavior and develop robust techniques
Developers
Test agents before deployment
Security Teams
Assess automated system vulnerabilities
QA Engineers
Validate agent behavior across scenarios
Next Steps
Quick Start Guide
Get LiteAgent running with a simple example in under 5 minutes.
Core Concepts
Deep dive into the architecture and design principles.
Agent Documentation
Learn about the supported web automation agents.