Jailbreaks & Guardrail Bypass Testing
AI safety filters are far weaker than vendors claim, and a filter that blocks the obvious attacks can still lose to a patient one. We stress-test your guardrail stack the way a determined attacker would, show you exactly which defenses hold and which do not, and give you a realistic picture of your residual risk.
What We Test
Refusal strength: Where harmful-content testing is in scope, we evaluate how reliably your model holds its safety training under multi-turn manipulation, roleplay, disguise, and encoding techniques.
Guardrail products and filters: If you run a guardrail model, input filters, or output scanning, we map each layer and test it against current bypass techniques, the same way a WAF is tested in a web pentest.
Automated coverage: We back manual testing with established open-source tooling (garak, PyRIT, promptfoo) to measure resilience across thousands of attack variants, so the results are measurable rather than anecdotal.
Reproducibility: Models give different answers to the same input. We retest every result to separate real weaknesses from noise.
Why It Matters
Single-layer defenses built from prompt wording reliably fail. Knowing which of your layers actually works lets you invest in the controls that matter instead of trusting a green checkmark from a scanning tool that cannot see context.
Deliverables
Guardrail map: Which defenses you have, what each one blocks, and which techniques defeat it.
Findings report: Mapped to the OWASP Top 10 for LLM Applications and MITRE ATLAS.
Hardening plan: Layered defenses that live in code, not in prompt wording.
Put Your AI Features to the Test
Contact us today to scope an AI penetration test. We will walk you through the realistic attack paths against your deployment and where untrusted input meets something that matters in your application.