AI's Secret Alignment: Time to Rethink 'Positive Backdoors'

Let's cut to the chase: The term 'positive backdoor' is misleading and needs to go. The AI/ML community has been tossing it around, often without rigorous backing. Instead, we should be looking at these trigger-activated behaviors as 'Secret Alignment', and they should be treated with a healthy dose of skepticism until proven otherwise.

The Problem with 'Positive Backdoors'

In the age of Private AI, where language models are turning into privately owned assets, security's a big deal. You're dealing not just with unauthorized access but also with the risk of model theft and misuse. Recently, some folks proposed 'positive backdoors' as a solution to these problems. It sounds good, but there's a catch. Many of these claims fall apart when you subject them to standardized tests. The demo is impressive. The deployment story is messier.

What we're seeing is a mishmash of covert trigger-behavior associations aiming to manage access, assert ownership, and enforce safety. But in practice, these mappings often lack reliability. They stumble on key fronts like confidentiality, integrity, and availability, the CIA of security. So, how solid are these 'positive backdoors'? Not very, as it turns out.

Why It Matters

The real test is always the edge cases. When push comes to shove, these systems are brittle. That's no small issue. Imagine having a lock on your door that works.. most of the time. Would you feel secure? AI systems are no different. They're only as strong as their weakest link, and right now, these backdoors are a glaring vulnerability.

It's time for the community to step up its game. We need a community-wide evaluation of these Secret Alignment claims, something that can stand up to scrutiny. Without it, we're treading on thin ice, risking the integrity of AI systems everywhere.

What's Next?

So, where do we go from here? For starters, let's retire the notion of 'positive backdoors' and focus on making Secret Alignment claims provable. That means rigorous, standardized evaluations that don't leave room for doubt.

In production, this looks different. We need systems that aren't just clever in theory but also rock-solid in practice. Until we get there, every 'positive backdoor' should come with a warning label.