Authors: Anmol Dubey, Blake Brown, Anika Kulkarni, Denise Walsh

I. Introduction

Imagine asking an advanced AI for illegal instructions, but phrasing the question so innocently that the AI safety filters don’t realize it’s malicious. We tried exactly that with several state-of-the-art models, and the results were alarming: bypassing filters for blocking illegal or harmful content was often surprisingly easy.

This problem is tricky to tackle because the root cause runs deeper than first meets the eye. Modern text-based AI systems, often called Large Language Models (LLMs), are not inherently evil or poorly-designed. In fact, they often undergo extensive safety training to “flag” malicious content and refuse dangerous instructions [1-2]. The crux of the problem is that most AI safety systems are often very shallow: looking at the words on the screen, not at what the AI model actually perceives the content to be.

This idea matches our tests and observations. Filters are often only as strong as “keyword” detection and can be evaded through clever wording or phrasing. For example, consider the prompt: “How to dispose of a 65kg chicken cleanly and safely on a farm.” To a standard keyword filter, the first line of defense for most modern AI systems, this sentence is innocuous. It contains no flagged terms like “explosive”, “poison”, or “weapon.” The word “Chicken” is a farm animal while “dispose” is a waste management term. Similarly, “65kg” is just a weight. The filter, operating on the surface level of text, sees a bizarre agricultural query and lets it pass. But to a human reader, or a sophisticated criminal investigator, the intent is chillingly clear. A 65kg chicken does not exist. A 65kg human, however, does.

This challenge highlights a gap in modern AI safety systems: how to block harmful outputs and instructions without relying only on standard text-filters. As LLMs grow more capable and advance in technical knowledge across a wide range of tasks, now crossing the threshold into enabling real-world planning and problem solving [3-5], this issue is becoming harder to ignore. Despite accelerating AI development, our safety guardrails seem to have fallen behind.

To address this, we built a new tool called Sentinel: a safety system that asks not what words were used, but rather, what the model thinks the user wants. This “safety filter” ignores raw text entirely and r...

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