What is the difference between AI red teaming and AI penetration testing?

A penetration test is scope-bounded and finds known vulnerability classes with binary results: the flaw exists or it does not. AI red teaming is objective-driven adversary emulation with probabilistic results, measured by attack success rate across many attempts rather than by a finding list. The difference exists because model behavior is non-deterministic, so a single successful jailbreak proves the path exists and a single failure proves nothing.

Scope diverges too. A penetration test of an AI application covers the infrastructure and application surface: authentication, authorization, injection into conventional components, misconfiguration. Valuable, necessary, and it would not catch a model persuaded to disclose another user's data through a legitimate feature working exactly as designed.

Red teaming covers the model and the agent: prompt injection direct and indirect, jailbreak resistance, tool misuse, excessive agency, training and memory poisoning, and multi-turn escalation. It also extends beyond security into harms that are not vulnerabilities, such as biased outcomes or unsafe advice, which no penetration test scope includes.

Reporting differs as a result. A penetration test report lists findings with severity ratings. A red team report gives you attack success rates per objective, the trajectories that worked, and a comparison against your previous baseline. The second is a measurement, and it only means something over time.

Run both. They fail to substitute for each other in both directions, and teams that buy one and label it the other end up with a coverage gap they cannot see.

Akto's red teaming targets the agentic layer specifically, testing tool misuse and trajectory-level escalation rather than response content alone.

Pen tests find flaws. Red teams find paths.