Reasoning-Model Penetration Testing
Reasoning models write out their thinking before they answer. That visible reasoning is an extra layer of text in the model's context, and like everything else in that context, it can be influenced. More reasoning does not mean more safety: in several documented cases it means a larger attack surface. We test whether your thinking model's safety holds up under reasoning-specific attacks.
What We Test
Reasoning dilution: Whether padding the model's context with long, benign reasoning can weaken its safety checks enough for harmful output to slip through.
Thinking-budget steering: Whether an attacker can force the model into extended reasoning, or cut it short, so that safety checks are skipped or weakened.
Chain-of-thought spoofing: Whether a fabricated reasoning trace in the context convinces the model that it already decided to comply.
Structured output coercion: Whether required output formats (JSON schemas, grammars, enums) can force restricted content out field by field, in a plane that prompt-scanning filters do not inspect.
Guardrail placement: We verify your defenses run on the final answer, not just the prompt, and after constrained decoding rather than before.
Why It Matters
Teams adopt reasoning models for better answers and assume better answers mean safer behavior. Research shows the opposite can be true: the safety logic can ride on the thinking process, and the thinking process can be steered. If your product uses thinking models, this surface needs testing before someone else tests it for you.
Deliverables
Findings report: Mapped to the OWASP Top 10 for LLM Applications and MITRE ATLAS.
Guardrail placement review: Where your checks run in the pipeline, and where they should.
Hardening plan: Practical controls for reasoning-model deployments.
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.