Poisoning attack

As explained in The Art of AI Product Development

An attack that deliberately corrupts or manipulates data used to train or operate a machine-learning system in order to compromise its behavior or outputs.

A poisoning attack deliberately corrupts training data so an AI system produces unreliable or harmful outputs. Because the corruption may remain hidden until deployment, it can damage customer trust as well as model integrity. section 11.1 section 11.1.1

DataMax recommendations

After launching an AI recommendation feature, DataMax finds that some customers receive nonsensical advice: a logistics company sees retail trends, while a healthcare client receives construction-related recommendations. The cause is incorrect examples contaminating the training data. The incident shows how a poisoning attack can surface as declining adoption and lost confidence rather than an obvious security alert. section 11.1.1

Reduce the risk and improve detection by:

• Validating incoming data for anomalies and inconsistencies before adding it to training sets. section 11.1.1
• Using trusted, verified sources and automated filters to remove suspicious or outlier data points. section 11.1.1
• Monitoring production performance and investigating unusual accuracy drops or unexpected outputs. section 11.1.1
• Carrying security controls across data movement, transformation, and combination during dataset preparation. section 11.1.1

Figure 11.1 Security needs to be managed at the data, intelligence, and user experience levels.

Place poisoning attacks within a broader security view spanning data, models, and production usage. section 11.1

How The Art of AI Product Development covers it

How deep it goes

Explains the attack, shows its product consequences, and gives validation, filtering, sourcing, and monitoring practices without detailing attack mechanics.

Examples

Uses DataMax recommendations, irrelevant industry advice, poisoned examples, production anomalies, and training-data validation as concrete illustrations.

What you can do after

Diagnose potential poisoning through anomalous data and output changes, then apply validation, filtering, trusted sourcing, and monitoring.

How it connects

Connects poisoning attacks with AI governance, data security, model monitoring, data exfiltration, and customer trust.

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Also explained in

• 

  liveBookmanning.com
  Introduction to Generative AI, Second EditionData poisoning is an attack that inserts malicious or misleading examples into training data so a model learns harmful or exploitable behavior.
• 

  liveBookmanning.com
  LLMs in ProductionData poisoning is an attack that inserts malicious or manipulated examples into training data to alter a model's behavior.

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Related concepts

• training datasetuses · A collection of examples or observations supplied to a learning procedure to fit or adapt a model or learn its patterns or parameters.
• validationaddressed by · The process of checking data, behavior, systems, models, or results against defined criteria to assess their correctness, suitability, or acceptability.
• backdoor attackproduces · A backdoor attack uses or introduces a concealed mechanism that bypasses intended controls to enable unauthorized access or trigger behavior under specific conditions.
• adversarial inputimplements · An input crafted, selected, or modified to exploit a system’s weaknesses or induce incorrect, unsafe, or otherwise unintended behavior.

This explainer was generated by Manning's knowledge graph from the text of The Art of AI Product Development. The section links point to the parts of the book it draws on.