What is trust & abuse engineering?

Definition

Trust & abuse engineering is the practice of identifying and removing the ways real users and automated actors misuse systems that are technically functioning as designed. It focuses on fraud, manipulation, and harm that emerges through legitimate features at scale — often without any exploit, breach, or obvious bug.

In other words: it’s what you do when “nothing is broken”, but something is still going wrong in production.

Where it sits in cybersecurity

Trust & abuse engineering sits at the intersection of product security, fraud prevention, trust & safety, and platform integrity. Traditional security disciplines often focus on vulnerabilities, access control, and perimeter risk. Trust & abuse engineering focuses on behaviour: how systems are used, where assumptions fail, and how harm propagates through workflows over time.

It’s not a replacement for application security or penetration testing — it complements them. Pen testing asks “can someone break in?”. Trust & abuse engineering asks “how does harm emerge through normal use at scale?”. Different questions lead to different fixes.

Why it matters now

Modern platforms have grown more complex. Products depend on automation, integrations, and increasingly AI-driven flows. This increases the speed at which misuse can spread, the difficulty of detecting it early, and the cost of getting it wrong.

The most damaging incidents are often not caused by novel exploits. They come from predictable patterns: thresholds that can be gamed, workflows that assume good intent, controls that don’t scale, and “valid” behaviour used for illegitimate ends — especially once automation or AI is involved.

What it looks like in practice

Trust & abuse engineering is hands-on. It typically involves:

• Mapping trust-critical workflows where third-party input can cause harm
• Modelling how fraud and abuse actors exploit incentives, assumptions, and thresholds
• Simulating real abuse paths against production-like flows (authorised)
• Tracing how harm propagates end-to-end through the system
• Implementing controls: guardrails, friction, rate limits, detection, escalation
• Re-testing to confirm the abuse path is genuinely closed

The goal is not perfection. The goal is to remove the paths that lead to repeat incidents, without breaking legitimate use.

How it differs from related work

A useful way to think about it is by the primary question each discipline asks:

• Application security: “Are there vulnerabilities in the code?”
• Pen testing: “Can an attacker break in or escalate access?”
• Fraud prevention: “How do we detect and reduce fraudulent activity?”
• Trust & Safety: “How do we reduce harm and enforce policy at scale?”
• Trust & abuse engineering: “How does harm emerge through legitimate product behaviour — and how do we remove the path?”

In practice there is overlap, but the emphasis is different: trust & abuse engineering lives closer to product workflows and focuses on repeatable failure modes caused by real-world behaviour.

Why it’s hard to outsource to tools

Tools can help with detection, logging, and automation. But the most important work here is contextual: understanding the product’s incentives, how users behave, how attackers adapt, and where controls should exist without harming legitimate use. Those decisions require system-level judgment and hands-on testing in real workflows.

Where Maple Logic fits

Maple Logic is a specialist practice focused on this discipline. We work with a small number of teams to identify and remove abuse paths in real systems — usually starting with an Abuse Surface Mapping, then hardening the highest-risk surfaces through focused engineering sprints.

If you’re dealing with fraud, misuse, or repeat incidents where nothing seems “broken”, we can help you understand what’s happening and what to do next.