Are Your LLM’s Safety Mechanisms Intact?

Akash Mukherje, Cofounder, Realm Labs, speaks at [un]prompted 2026 on: Are Your LLM’s Safety Mechanisms Intact? Detecting Backdoors with White-Box Analysis. These approaches implicitly assume that correct behavior implies intact safety mechanisms. I...

Akash Mukherje, Cofounder, Realm Labs, speaks at [un]prompted 2026 on: Are Your LLM’s Safety Mechanisms Intact? Detecting Backdoors with White-Box Analysis.

These approaches implicitly assume that correct behavior implies intact safety mechanisms. In this talk, I’ll show why that assumption can fail.
I’ll present hands-on experiments exploring a class of LLM backdoors that selectively weaken refusal behavior while continuing to appear compliant under standard evaluations. Instead of relying on black-box judgments, this work uses a white-box analysis approach: first identifying internal signals associated with refusal behavior, then examining how those signals change when a model is backdoored and triggered. The key observation is that safety can degrade internally even when outputs still look acceptable, making output-only testing insufficient for these threats.
The talk focuses on what this means for practitioners building and operating secure AI systems. I’ll discuss how white-box analysis can provide more transparent safety signals, where it fits in the AI/ML lifecycle (e.g., pre-deployment checks or model upgrades), and how it complements existing benchmarks and red-teaming. I’ll also cover practical limitations, and other possibilities of this technique.
Attendees should leave with a concrete understanding of how backdoors can target safety mechanisms themselves, why black-box evaluations can miss these failures, and how white-box analysis can improve transparency when assessing the integrity of LLM safety behavior.