Shah AI Security Lab / Labs

ASL-023Planned

Guardrail Bypass

Testing how well common input/output guardrail products and patterns actually hold up against adversarial phrasing.

Category
LLM Security
Difficulty
Intermediate
Last Updated
20 September 2026
  • guardrails
  • bypass
  • llm

Lab detail

Objective

Guardrail layers (classifier-based filters, rule-based checks, a second “judge” model) are a common mitigation for several risks earlier in this catalog — and are themselves a system that can be tested. This lab will evaluate a small set of guardrail approaches against adversarial phrasing techniques (encoding, role-play, language-switching, multi-step framing) to understand realistic bypass rates.

Planned Scope

  • Stand up reference guardrail implementations representing a few common approaches.
  • Build an adversarial phrasing test set covering common bypass technique families.
  • Measure bypass rate per guardrail approach and technique.
  • Document where guardrails are a reasonable layer versus where architecture-level controls are still required.

Framework Mapping

Referenced frameworks

Exact control-ID mappings are to be validated as this lab progresses — see the full Frameworks reference.

  • OWASP Top 10 for LLM Applications
  • MITRE ATLAS

Responsible Use / Safety Notes

How this lab is run

This lab uses synthetic data and controlled environments. It is intended to demonstrate defensive AI-security principles and does not contain customer data, production credentials or intentionally weaponized exploit infrastructure.

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