AI for third-party risk.
Put it to the test.

Putting an AI agent into your vendor-risk program without testing it is like giving a new employee privileged access on day one and hoping for the best. The TPRM Benchmark answers two questions every security leader should be asking: can this model do the work, and can it be turned against me? If you can’t answer both, you don’t have an AI advantage. You have a new attack surface.

Our initial benchmark runs show open-weight models closing the gap with leading frontier models on supply chain security tasks, but several of those same models remain highly susceptible to adversarial attack. As AI increasingly takes on critical security functions, organizations need to know not just which models perform best at each task, but which ones hold up when someone is actively trying to manipulate them.

You can't secure an attack surface you haven't measured, and TPRM Bench gives security teams a shared, transparent way to evaluate models on capability and attack resistance together. In our own Snyk research across 3,000 enterprise environments, 74.5% of those with a detected model, reference at least one that scores ‘high’ on an attacker goal under adversarial testing.

There are many AI models on the market that deliver the results they promise, but very few adhere to the cybersecurity standards needed for real industry adoption, and that gap is exactly why we need an evaluation platform like this one. I think of a good benchmark like a race track: it shows you which models have real problems under the hood, and it reveals the true limits of what’s possible, which is what makes this work academically meaningful, about scientific discovery, not just about picking the best commercial model off the shelf. It also matters enormously for organizations trying to use AI in third-party risk workflows specifically. It’s not enough to know a model can complete the task. Done right, a benchmark like this is how you get the confidence to actually deploy AI in these workflows, knowing it does the job well and that its security can be trusted.

TPRM Bench stands out by testing AI against real supply chain scenarios grounded in empirical security data. Its rigorous, reproducible framework helps bridge the gap between benchmark performance and real-world security as AI adoption in critical infrastructure accelerates.

Preempting the next cyber attack means proving your defenses before they're put to the test. Trust in AI must be earned through continuous testing in realistic, production-like conditions. SecurityScorecard's TPRM Benchmark helps build that trust by measuring whether AI can perform critical tasks and resist manipulation. For security leaders, validating AI is essential for deciding how much autonomy to give agents and for pinpointing when humans need to remain in control.

AI is now part of both attacker and defender tooling, so unexpected model behavior can quickly become an incident for one side or an opportunity for the other. At GreyNoise, we've seen attackers' AI use leave identifiable tells, and we've seen defenders' models make mistakes analyzing packets and TTPs. Objective, transparent measurement like SecurityScorecard's TPRM AI Benchmark puts evidence of model performance on the table, so defenders can adopt AI with a clear view of its strengths and limits.