Because nobody has figured out how to make that work 100% reliably yet. The current approach is to use delimiters that are special tokens that can't be represented in regular text: https://github.com/openai/harmony/blob/main/docs/format.md#s... Then you train your model to take those tokens into account. Which sounds promising... until you see results like this one: https://arxiv.org/abs/2603.12277 > We trace prompt injection to role confusion: models perceive the source of text from how it sounds, not its labeled role. A command hidden in a webpage hijacks an agent simply because it sounds like <user> text, despite its <tool> label
Prompt Injection as Role Confusion
Charles Ye, Jasmine Cui, Dylan Hadfield-Menell
2026 · arXiv
- HN score
- 0
- Mentions
- 4
- Readers
- 3
- Months
- 3
From the abstract
LLMs see the world as a single stream of text, partitioned into roles like or . We trace prompt injection to role confusion: models perceive the source of text from how it sounds, not its labeled role. A command hidden in a webpage hijacks an agent simply because it sounds like text, despite its label. We design role probes to measure how LLMs internally perceive "who is speaking," and find that injected text occupies the same representational space as the trusted role it imitates. We demonstrate this with CoT Forgery, a zero-shot attack that injects fabricated reasoning into user prompts and tool outputs. Models mistake the forgery for their own thoughts, yielding 60% attack success against frontier models with near-zero baselines. Strikingly, the degree of role confusion predicts attack success before a single token is generated. This mechanism generalizes beyond CoT Forgery to standard agent prompt injections, revealing prompt injection as a measurable consequence of role perception. To the model, sounding like a role is indistinguishable from being one. Project page and writeup: https://role-confusion.github.io
Metadata: arXiv. Abstracts retain their original authors’ rights.
In the conversation
Original HN comments, with labels from simple text rules. A label describes the whole comment and may miss context or irony. Read the discussion to make up your own mind. About the labels
4 selected excerpts · 4 total mentions
People will keep hiding reasoning because it allows prompt injection https://arxiv.org/pdf/2603.12277, in addition to facilitating distillation (you don't pay the full cost of RL)
I believe you're right, it's an issue of the model misinterpreting things that sound like user message as actual user messages. It's a known phenomenon: https://arxiv.org/abs/2603.12277
>Several people questioned whether this is actually a harness bug like I assumed, as people have reported similar issues using other interfaces and models, including chatgpt.com. One pattern does seem to be that it happens in the so-called “Dumb Zone” once a conversation starts approaching the limits of the context window. I also don't think this is a harness bug. There's research* showing that models infer the source of text from how it sounds, not the actual role labels the harness would provide. The messages from Claude here sound like user messages ("Please deploy") rather than usual Claude output, which tricks its later self into thinking it's from the user. *https://arxiv.org/abs/2603.12277 Presumably this is also why prompt innjection works at all.
Keep following the thread.
More in PhysicsMore from the same topics.
Sparks of Artificial General Intelligence: Early experiments with GPT-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan +11
Artificial intelligence (AI) researchers have been developing and refining large language models (LLMs) that exhibit remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition.
Talent vs Luck: the role of randomness in success and failure
A. Pluchino. A. E. Biondo, A. Rapisarda
The largely dominant meritocratic paradigm of highly competitive Western cultures is rooted on the belief that success is due mainly, if not exclusively, to personal qualities such as talent, intelligence, skills, efforts or risk taking.
The Ghost in the Quantum Turing Machine
Scott Aaronson
In honor of Alan Turing's hundredth birthday, I unwisely set out some thoughts about one of Turing's obsessions throughout his life, the question of physics and free will.
Categorizing Variants of Goodhart's Law
David Manheim, Scott Garrabrant
There are several distinct failure modes for overoptimization of systems on the basis of metrics.