Concern 188 · Working draft · editorial review in progress
Detector evasion
Detector evasion means deliberately changing harmful or prohibited material so that a detection system misses it.
Understanding the concern
An attacker can vary the form of a message, file, or generated output while trying to preserve its purpose. AI may help create or evaluate variations. This differs from an ordinary detection error because the input is shaped with the detector's weakness in mind.
The result depends on the detector and the attacker's access to feedback. Success against one test does not establish invisibility to all defenses. Combining different evidence and examining behavior can be more resilient than relying on a single content pattern.
An example
A misleading message keeps its meaning while changing the features that a simple filter was trained to recognize.
Illustrative unless identified as a documented case.
Words in plain English
- AI
- Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.
Evidence and its limits
NIST describes evasion mechanisms; detector research examines limits under rewriting and related conditions. Findings are detector- and task-dependent.
NIST · 2025 · Technical report
Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations ↗ (opens in a new tab)Defines attack objectives and mechanisms across AI lifecycle stages. Taxonomy categories are not estimates of incident frequency.
Sadasivan and colleagues · Research study
Can AI-Generated Text be Reliably Detected? ↗ (opens in a new tab)Examines theoretical and empirical detection limits; does not establish that every detection task is impossible.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
Are we testing against deliberate adaptations, or only against yesterday's examples?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Stewardship
Stewardship means caring for resources and systems that others depend on, including people who will use them later. It looks beyond the benefit of a quick launch.
Plan for maintenance, energy use, data protection, and eventual shutdown. Ask who will look after the tool when the original team moves on and how future users can avoid inheriting hidden costs.
Ask together: What are we responsible for caring for over time?
Prudence
Prudence means using careful judgment before and during action. It balances worthwhile opportunities with the need to avoid harm that could have been anticipated.
Start with a limited trial, set stopping points, and prefer choices that can be undone when uncertainty is high. Review what happens rather than assuming the original plan remains sound.
Ask together: What is a careful next step, and when should we stop or change course?
Accountability
Accountability means being answerable for decisions and taking responsibility for repair. It requires more than saying that the software made the choice.
Name the person or team responsible, keep useful records, and give them authority to correct problems. Make the route to a complaint clear to the people affected.
Ask together: Who will answer for the outcome and help put things right?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
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