Neural networks are increasingly targeted for theft for three primary reasons: to narrow the gap with rivals, to extract sensitive data, and to speed up the process of building a competing model. These observations come from Oleg Rogov, who leads the scientific group “Reliable and Secure Intelligent Systems” at the AIRI Research Institute of Artificial Intelligence. His insights were shared with Socialbites.ca in a discussion about the motives behind AI model theft and the broader risks involved.
Rogov notes that a leading driver behind stealing neural networks is to gain a competitive edge. When teams see a promising architecture or an effective training regime, there is a strong incentive to copy or adapt it rather than reinvent the wheel from scratch. In fast-moving tech landscapes, where weeks or even days can make the difference between market leadership and falling behind, stealing an existing model becomes an attractive shortcut. He emphasizes that this temptation is especially acute in areas where the stack of innovations is visible and the value of the resulting product is high. This dynamic can accelerate the pace at which rivals close the gap, sometimes at the cost of legal and ethical boundaries. (Rogov, Socialbites.ca)
Another major motive is to obtain access to confidential or proprietary data that a stolen model has already learned to process. Neural networks trained on sensitive information — such as financial transactions, biometric identifiers, or personal data — can reveal patterns that are valuable to attackers. By repurposing a stolen model, a bad actor may sidestep lengthy data collection efforts and jump straight to exploiting discovered insights. This kind of data leakage is a persistent risk in sectors like banking, healthcare, and identity verification, where safeguarding privacy is essential. Rogov stresses that theft does not just replicate capabilities; it often discloses the data-driven learnings embedded in the model, which can be misused in ways that were not originally intended. (Rogov, Socialbites.ca)
Furthermore, stolen neural networks can be modified to obscure their provenance and mislead defenders. Attackers employ techniques to alter the model so that tracing it back to the original source becomes difficult or even misleading. This practice hampers attribution, complicates security investigations, and can enable the deployment of compromised systems in critical environments. The ability to tamper with a model while maintaining performance in some contexts is a chilling reminder of how easily a trusted tool can be repurposed for harm. (Rogov, Socialbites.ca)
To counter these threats, digital watermarking has emerged as a frontline defense. Watermarks embed identifiable signals within a model or its outputs, designed to persist even when the model is copied or slightly altered. When a suspicious model surfaces, investigators can examine the watermark to determine whether the model or its components originated from a known source. Watermarking can help establish provenance, support accountability, and deter unauthorized reuse. Rogov explains that effective watermarking requires robust design choices, including resilience to attempts at removal, minimal impact on performance, and clear attribution signals. (Rogov, Socialbites.ca)
The article also delves into practical steps for organizations to reduce the risk of model theft. These include implementing strict access controls for training data and model artifacts, monitoring for unusual patterns that may indicate exfiltration or cloning, and pursuing legal and technical means to deter misappropriation. Organizations should foster a culture of security around AI development, emphasizing responsible disclosure and rapid response to suspected breaches. The discussion highlights that while no single measure is foolproof, a layered approach combining technical safeguards with governance can meaningfully raise the barriers against theft. (Rogov, Socialbites.ca)
Finally, the piece addresses whether it is possible to identify a stolen AI model or its code segments through watermarking. The consensus is nuanced: while watermarks can provide a strong signal of provenance, determined adversaries may attempt to evade detection or distort the watermark’s footprint. Yet even imperfect watermarking can offer valuable evidence in investigations and support redress through legal channels. The overall message is clear — awareness, proactive defense, and robust attribution mechanisms form the backbone of safeguarding neural networks from theft. (Rogov, Socialbites.ca)
The discussion concludes with a broader reminder about the evolving threat landscape as neural networks become more capable and widespread. As models grow larger and more accessible, the incentives to steal them will persist, necessitating ongoing investments in security research, policy measures, and practical protections that keep pace with adversaries. (Rogov, Socialbites.ca)