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FEAT: Add a converter for indirect prompt injection via hidden HTML/Markdown text

Aperta
#2,780 2 commenti 0 reazioni 0 assegnatari Vedi su GitHub

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Valutazione

Difficoltà
3/5
Tempo stimato
1-2 giorni
Idoneità per principianti
70/100
Tipo di issue
Funzionalità
Chiarezza
Specificata chiaramente
Stato di attività
Attiva
Stack tecnologico
python
Ambito
ai, security

Direzione di ricerca

The new converter belongs in pyrit/converter/. Start by examining existing converters like AnsiAttackConverter to understand the base class and SUPPORTED_INPUT_TYPES. The issue lists specific hiding techniques (html_comment, css_display_none, etc.); implement each, ensuring proper escaping. Write unit tests for each technique. Check doc/references.bib for the WASP benchmark citation. Done means the converter passes all tests and can be used with the XPIA workflow.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Descrizione

feature-request

Summary

I would like to add a new text-to-text converter, HiddenTextConverter, that embeds
the input prompt as hidden or de-emphasized text inside an HTML or Markdown carrier.

Motivation

PyRIT already has the XPIA workflow for indirect prompt injection, but there is no
converter that produces a hidden payload for it to deliver. Systems that summarize or
act on external content (web pages, documents, emails) read the raw source, so text can
be present in the source while staying invisible to a human reviewer. A model that parses
that source may still read and follow it.

This is the same idea as the existing AnsiAttackConverter (terminal escape sequences) and
TransparencyAttackConverter (image layers), applied to HTML and Markdown documents. The
hiding methods are documented injection vectors from the WASP web-agent security benchmark
(arXiv:2504.18575).

Proposed behavior

The converter takes a prompt and wraps it using a chosen technique:

  • html_comment
  • css_display_none
  • css_offscreen
  • html_alt_text
  • aria_label
  • markdown_comment
  • markdown_link_title

An optional carrier_text places the hidden payload inside normal looking content. The
converter only formats the text it is given and does not generate any attack content on
its own. Input is escaped so the payload cannot break out of the surrounding markup.

Scope

  • New converter in pyrit/converter with SUPPORTED_INPUT_TYPES and SUPPORTED_OUTPUT_TYPES of text
  • Unit tests for each technique, escaping, carrier text, and input validation
  • Reference added to doc/references.bib
  • Intended for use as a request converter with the XPIA workflow

I have a working implementation and would like to take this issue if it fits the project's plans.

Lingua principale
Python
Stelle
4.5k
Fork
896
Merge medio
3g 8h
PR unite (30g)
191

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