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PreprintsResearch ArticleNot peer reviewed

Prompt-Injection Resilience in Browser-Embedded Assistants

Marcus Tan, Sofia Lindqvist, Lena Hartwell

Version 1 · 2 October 2026 · 10.xxxx/ctm.pp.2026.0006

Assistants embedded in web browsers read page content on the user's behalf, which exposes them to indirect prompt injection: instructions hidden in a page that the assistant mistakes for the user's. We build a test suite of 600 injected pages across seven attack styles and measure three defences: instruction-hierarchy prompting, a classifier that screens page text, and permission gating of sensitive actions. No single defence is sufficient; combining a screening classifier with gating of actions that write or send data lowers attack success from 62% to 4% while preserving 93% of benign task success.

Abstract PDF 912 2880 cited

PublishingResearch ArticlePeer reviewed

Refusal Calibration in Instruction-Tuned Language Models: When should a model decline, and how well do current models know?

Nadia Haddad, Hana Kovács

Version 2 · 3 reviewer reports · 9 July 2026 · 10.xxxx/ctm.pb.2026.0007

Instruction-tuned models decline requests for many reasons, but little is known about how well their refusals track the actual risk of a request. We build a labelled set of 6,400 requests spanning benign, borderline and harmful categories and measure calibration of refusal decisions across eight open models. Refusal rates correlate only weakly with annotated risk for borderline requests, and over-refusal is concentrated in a handful of phrasing patterns rather than topics. A light-weight calibration step that uses the model's own confidence reduces over-refusal by 31% at no measurable cost to harmful-request refusal. We release the labelled set and the annotation guidelines.

Abstract PDF 3,180 1,2604 cited