Agentic data science with the Data Agent Kit
cloud.google.com·8h ago
AI Research
arXiv CS.AI·Aug 26·5 min read
ADVERSA: Measuring Multi-Turn Guardrail Degradation and Judge Reliability in Large Language Models
TL;DR
ADVERSA is an automated framework that evaluates the safety of large language models (LLMs) through multi-turn interactions, revealing a 26.7% jailbreak rate concentrated in early rounds. It measures guardrail degradation and judge reliability, providing a nuanced understanding of model vulnerabilities.
✦ Why It Matters
Implement ADVERSA to rigorously evaluate your LLMs' safety under sustained adversarial conditions.
Key Takeaways
- Use ADVERSA to assess LLM safety over multiple interactions.
- Focus on early rounds for potential jailbreak vulnerabilities.
- Measure judge reliability to improve evaluation accuracy.
- Consider attacker drift and refusals in your assessments.
How It Works
ADVERSA employs a fine-tuned 70B attacker model to simulate adversarial interactions with LLMs, measuring compliance through a structured 5-point rubric. This allows for continuous tracking of guardrail degradation across multiple conversation rounds, rather than relying on binary pass/fail outcomes.