From the course: CompTIA SecAI+ (CY0-001) Cert Prep
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Model inversion
From the course: CompTIA SecAI+ (CY0-001) Cert Prep
Model inversion
Model inversion attacks try to use prompts to extract information about the data a model was trained on. Sometimes the goal is to recover actual examples from the training set. In other cases, the goal is to infer sensitive attributes or reconstruct inputs based on the model's responses. The model itself becomes the attack surface, even when the attacker cannot access the underlying data directly. These attacks take advantage of the way that models memorize information. If training data contains personal, medical, or proprietary content, an attacker can sometimes coax the model to repeating pieces of that training content. The model may not intend to memorize this sensitive information, but large-scale training on data that was not cleaned or filtered can leave traces that leak through to answers to prompts. One model inversion attack technique uses repeated queries with small variations in each query. The attacker keeps prompting and looks for consistent patterns that reveal hidden…
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Contents
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The AI lifecycle1m 39s
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Business alignment in the AI lifecycle1m 43s
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Data collection2m 20s
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Data preparation3m 15s
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Model development and selection2m 13s
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Model evaluation and validation2m 29s
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Model deployment and integration3m 25s
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Monitoring and maintenance3m 19s
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Manipulating application integrations4m 8s
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AI supply chain attacks2m 4s
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Insecure plug-in design2m 9s
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Insecure output handling1m 23s
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Output integrity attacks2m 8s
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Model denial of service1m 31s
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Excessive agency1m 33s
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Overreliance1m 34s
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AI hallucinations1m 4s
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Monitoring prompts and responses2m 51s
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Log monitoring4m 30s
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Rate and cost monitoring5m 1s
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Auditing for AI hallucinations3m 33s
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Auditing for accuracy3m 29s
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Auditing for bias and fairness4m 35s
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Auditing access and security compliance3m 48s
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Responsible AI5m 29s
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AI risks2m 23s
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Introduction of bias2m 37s
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Accidental data leakage2m 53s
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Reputational loss2m 11s
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Accuracy and performance of the model2m 22s
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Intellectual property risks3m 31s
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Autonomous systems2m 27s
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Shadow IT and shadow AI1m 48s
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Awareness training2m 21s
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