---
id: "concept-model-inversion-attacks"
type: "concept"
source_timestamps: ["§ New Risks Executives Must Address"]
tags: ["threat-vectors", "intellectual-property", "data-theft"]
related: ["concept-data-poisoning"]
definition: "Attacks where hackers extract sensitive training data or reconstruct proprietary algorithms from a deployed AI model."
source_title: "Research: Conventional Cybersecurity Won't Protect Your AI"
source_url: "https://hbr.org/2026/01/ts-research-conventional-cybersecurity-wont-protect-your-ai"
sources: ["tail2"]
sourceVaultSlug: "hbr-seg-tail2"
originDay: 2
articleStem: "hbr-tail-128-cybersecurity-wont-protect-ai"
sourceUrl: "https://hbr.org/2026/01/ts-research-conventional-cybersecurity-wont-protect-your-ai"
sourceTitle: "Research: Conventional Cybersecurity Won’t Protect Your AI"
---
# Model Inversion Attacks

Model inversion attacks occur when hackers extract sensitive **training data** or reconstruct **proprietary algorithms** directly from a deployed AI model. For organizations whose core intellectual property is a unique dataset or a custom-trained model, model inversion is a **direct theft of competitive advantage** — the model itself can be reverse-engineered to expose the foundational assets used to build it. It rounds out the new-risk trio with [[concept-data-poisoning]] and [[concept-adversarial-prompts]].

**Enrichment grounding.** The characterization aligns with established ML-security research: model inversion, together with related **membership inference** and **model extraction** attacks, is widely documented — adversaries can infer training data or replicate model behavior from API access. This threat is distinct from [[concept-echoleak|EchoLeak]] and is not tied to it in the source material, but the definition is accurate.