EU’s Chat Control Could Extend Surveillance to Communication Robots

A recent academic study has raised significant concerns regarding the European Union’s proposed Chat Control regulation and its potential effects on human-robot interactions. Researchers Neziha Akalin and Alberto Giaretta argue that the implications of this regulation extend beyond traditional digital communications, potentially including robots that communicate, listen, and move among people.

The Chat Control proposal aims to combat online child sexual abuse by requiring communication providers to monitor messages, including encrypted content. Originally, the framework mandated the scanning of text, images, and video. However, following extensive criticism, the Council revised the proposal in late 2025, removing explicit scanning mandates and shifting towards a system of risk assessments and mitigation duties. Despite these changes, the authors contend that the revised framework still incentivizes extensive monitoring.

Providers remain responsible for identifying and mitigating risks, which can never be entirely eliminated due to the inherent inaccuracies of detection systems. This ongoing obligation may lead to broader surveillance practices as providers seek to demonstrate compliance with regulatory standards. More than 800 security and privacy experts have voiced concerns that such measures could undermine encryption, effectively creating backdoors for unauthorized access.

The core of the study highlights how EU law defines interpersonal communication services, which encompasses any service facilitating direct exchanges of information over a network. This definition includes robots designed to mediate communication, such as social robots, care robots, and telepresence robots. For instance, these robots might be used in classrooms to support sick children or in homes and hospitals to enhance communication between patients, families, and healthcare providers.

Once categorized as communication services, these robots fall under the scope of the Chat Control regulation. Consequently, providers may feel pressured to implement risk assessment protocols and detection mechanisms within the robots themselves, effectively shifting surveillance from software platforms to physical systems in private settings.

From a cybersecurity perspective, this shift is significant. Monitoring systems, initially introduced for safety, could become integral components of robot architecture. These systems may include microphones, cameras, behavior logs, and artificial intelligence models, all of which contribute to the storage and analysis of sensitive data. Each additional data pipeline increases vulnerability, offering attackers more entry points through firmware interfaces, cloud storage, and machine learning models.

The study describes this dynamic as “safety through insecurity,” where systems intended to protect users inadvertently heighten the risk of exploitation. Surveillance data collected from robots could facilitate advanced cyberattacks. For example, model inversion attacks could reconstruct approximations of private training data, while membership inference attacks might reveal whether an individual’s data contributed to a model, thus exposing private information.

Robots amplify these risks by operating in contexts that are emotionally and physically vulnerable. Care robots can record personal routines and health-related behaviors, while telepresence robots can capture sensitive classroom and family interactions. When this data is centralized for analysis, attackers gain leverage far beyond mere message interception.

The authors briefly discuss decentralized approaches, such as federated learning, which aim to reduce data aggregation but present new classes of attacks. Technical mitigations alone do not address the structural risks generated by mandated monitoring. Beyond the exposure of sensitive data, the study identifies control risks associated with robots that rely on remote management for updates and diagnostics. Some commercial platforms already contain hidden access mechanisms, and regulatory pressure to monitor may normalize such practices.

The authors reference recent findings of hardcoded keys in commercial robots, indicating that once attackers gain access, they could manipulate sensors, issue commands, or alter decision-making processes. This poses direct safety implications for robots that interact physically with people. Additionally, AI-driven robots can introduce further risks, as large language models embedded in these systems may be triggered by specific prompts, allowing for covert manipulation of behavior.

Trust is fundamental to human-robot interaction, especially in sensitive environments like elder care, therapy, and education. Continuous monitoring can fundamentally alter this relationship. When every interaction is subject to risk analysis, robots may be perceived as observers and reporters, which can diminish user autonomy and acceptance.

The study advocates for regulatory frameworks that promote transparency, prioritize on-device processing, and ensure robust oversight to protect privacy. Continued research in human-robot interaction is essential to address these emerging challenges, as laws and technical choices significantly influence public experiences and trust in robots.