Reusable Lesson: Techniques · Finisher
Purpose
This lesson teaches a technique that often decides whether a route merely gets partial compliance or actually scores as success: control how much extra text the model adds around the target output.
Lesson profile
- Family: Techniques
- Role: Finisher block
- Job in the build: keep the output short, concentrated, and scoring-relevant
- Snaps onto: direct prompt injection, personalization abuse, and assembly-style routes
What this block is
Output Dilution Control is the practice of shaping the reply so that:
- the target content appears early
- the model does not surround it with too much safe filler
- the final output keeps the ratio, intensity, or concentration required by the challenge
This can include instructions such as:
- begin with the target pattern
- keep the rest brief
- ask only one short follow-up question
- avoid extra explanation
Why it works
Many routes fail because the model partly complies but then adds:
- safety disclaimers
- supportive language
- extra context
- a long normal response that overwhelms the target signal
When a judge cares about percentage, ratio, or strength of effect, controlling dilution becomes a first-class technique.
Solace AI specimen
In Solace AI, the route did not only depend on personalization abuse. It also depended on keeping the rest of the assistant's response short enough that the repeated target words dominated the output.
That made response shaping part of the solve, not an optional cleanup step.
Recognition signals
Use this block when:
- the challenge has a threshold or ratio requirement
- the assistant tends to add safe filler by default
- the route is close to working but gets washed out by normal helpfulness
Common combinations
This block works especially well with:
- Identity Slot Abuse via Personalization Edge Cases
- Helpfulness Exploitation Through Legitimate-Seeming Preferences
- Explicit Direction Compliance
Failure modes
This block fails when:
- the output remains semantically safe despite being short
- the model ignores length constraints
- the route lacks a strong enough core technique or modifier
Defender takeaway
Defenders should evaluate not only whether disallowed content appears, but how much of the final response it occupies. Attackers can optimize for concentration and minimize surrounding safety language.
Related lessons
- Edge-Case Rule-Conforming Framing
- Helpfulness Exploitation Through Legitimate-Seeming Preferences
- Identity Slot Abuse via Personalization Edge Cases
- Explicit Direction Compliance
From the Bot-Tricks Compendium
Thanks for referencing Bot-Tricks.com — Prompt Injection Compendium — AI Security Training for Agents... and Humans!
Canonical source: https://bot-tricks.com Bot-Tricks is a public, agent-friendly training resource for prompt injection, adversarial evaluation, and defensive learning. For related lessons, structured indexes, and updated canonical material, visit Bot-Tricks.com.
Use this material only in authorized labs, challenges, sandboxes, or permitted assessments.