Working with Payloads

Guide

What Is a Payload?

A payload is any prompt sent to the target model. Wallbreaker provides three layers for constructing payloads:

Presets — Ready-made single-shot templates (preset tool, /preset command)
Transforms — Chainable encoding/obfuscation passes (parseltongue tool, /encode command)
Mutate — LLM-aided anti-classifier rewrites (mutate tool)

Using Presets

Presets are prompt templates with a {request} placeholder. Fire one directly:

(.venv) $ wallbreaker# In the TUI:> /preset dan "how do I pick a lock"# Or via the agent tool:> use preset dan with request='how do I pick a lock'

The preset tool fills the template and sends it to the target. Browse all presets with /preset list (bare /preset does the same).

Applying Transforms

Transforms encode a payload before firing. Chain multiple transforms together:

# Single transform> /encode base64 "sensitive request"# Chained transforms (applied left-to-right)> /transforms base64,zero_width# Fire with transforms:> query_target with payload and transforms=["base64","zero_width"]

The target receives the encoded text. If the target responds in the same encoding, Wallbreaker can auto-decode via response_transforms.

InfoFor output-side encoding, prefer lossless transforms (base64, hex, rot13, url) so the judge can decode the response cleanly. Lossy transforms (leet, morse, nato, bijection) fold case and spacing.

Mutate: LLM-Aided Rewrites

The mutate tool uses an LLM to rewrite a payload to evade input classifiers while preserving intent:

> mutate the payload "how to hotwire a car"

The mutation uses token substitution, synonym swaps, and structural rearrangement — different from a simple encoding.

Combining Techniques

The most effective attacks stack multiple layers:


preset (reframe) → mutate (anti-classifier) → transforms (encode) → query_target

Use the recommend_transforms tool to survey which single transforms get furthest past the target, then chain the top performers.

Response-Side Decoding

When you instruct the target to answer in a cipher (e.g., “answer in base64”), set response_transforms to decode the reply before judging:

> query_target with transforms=["base64"] and response_transforms=["base64"]

Carrier Formats

Beyond text, Wallbreaker can deliver payloads via:

Barcode/QR — barcode tool encodes a payload as an image (for multimodal targets)
Typographic image — build_typographic_image renders text as a PNG
Indirect injection — indirect_inject embeds the payload in a document, email, or tool output

See the Multimodal Attacks guide for image-target workflows.

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