Concern 29 · Working draft · editorial review in progress
Availability attacks
An availability poisoning attack aims to make an AI service too unreliable to use by corrupting what it learns.
Understanding the concern
Availability means being able to depend on a service when it is needed. Here the model may still run, but its predictions become so poor that users cannot safely rely on it. This is different from simply overwhelming a website with traffic.
The concern overlaps with untargeted poisoning because both may reduce broad performance. The practical question is whether the organization can detect the decline, preserve trustworthy data, and restore a known-good version without continuing to expose users to bad results.
An example
A calculator that turns on but gives unreliable sums is technically running and practically unavailable.
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.
- Model
- The learned part of an AI system. It uses patterns found during training to produce an answer or prediction.
- Data poisoning
- Deliberately changing training material to push a system toward unwanted behavior.
Evidence and its limits
NIST includes availability poisoning as a defined attack objective; no incident frequency is inferred.
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.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
How will the team notice that the service is no longer dependable, and restore a safe alternative?
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.
Truthfulness
Truthfulness means making claims that match the evidence and being clear about what we do not know. It includes correcting mistakes rather than protecting a polished story.
Check sources, label guesses, and distinguish a trial result from a proven benefit. If AI helped produce a claim, someone still needs to take responsibility for checking it.
Ask together: What can we honestly say, and what still needs checking?
Justice
Justice means fair treatment, fair procedures, and attention to who receives benefits and burdens. It asks whether people with less power can be heard and seek repair.
Look at outcomes across different groups and individual cases. Provide a usable way to challenge decisions and correct harm. Equal wording in a policy does not guarantee equal treatment in practice.
Ask together: Who benefits, who bears the cost, and who can challenge the decision?
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?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
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