As multimodal large models move from concept verification to commercial implementation, AI Agents are gradually deepening their involvement in device underlying operations and demonstrating the ability to independently execute complex cross-application tasks. Against this backdrop, some 'intrusive agents' that bypass standard application programming interfaces (APIs) and directly use system-level privileges to interfere with application execution have rapidly proliferated, posing unprecedented challenges to the coordination of existing internet trust mechanisms and ecosystems.
Based on a systematic research on the global and Chinese AI Agent markets, Frost & Sullivan (hereinafter referred to as 'Frost & Sullivan') has officially released the 'Intrusive Agent Industry Governance White Paper 2026'. This report focuses on the impact of intrusive agent mechanisms on industry traffic distribution, business ecosystem operations, and underlying data security, and makes forward-looking assessments of the industry's future standardization and governance paths.
PART.01
Intrusive agents rely on system-level permissions to break through application boundaries, posing new risks to industrial governance.
The key feature of intrusive agents lies in their reliance on system-level permissions rather than standard interfaces or protocols. They directly read interface information and identify page content using system-level signatures, and simulate user actions such as clicking, inputting, and navigating. With this approach, agents can execute tasks continuously across multiple software applications without obtaining business authorization from the application side. Compared to collaborative paths based on standard interfaces, although this method reduces the dependency on cross-application collaboration for ecological cooperation, it also breaks through the original application boundaries and permission boundaries, moving risks to the system level.
In the past, users had to enter specific applications first and then gradually complete information acquisition and operational decisions; now, system-level agents are taking over user intent understanding, task decomposition, and action scheduling, while applications have largely become the interface for task execution. As the original application-centric entry logic on mobile devices has been rewritten, system-level agents, while enhancing task execution convenience, are also beginning to have a new impact on the existing ecological order, permission rules, and governance boundaries.
Definition of intrusive agent
Source: Analysis by Frost & Sullivan
PART.02
The intrusive Agent pre-empts users' decision-making process, putting pressure on the commercial value of third-party applications.
As intrusive agents gradually become the main entry point for users to initiate tasks, the role of traditional mobile applications in the ecosystem has also changed. Behaviors such as search, browsing, comparison, clicking, and placing orders, which were originally handled by the application itself, are increasingly being moved to the agent side for completion. This has significantly reduced the direct interaction time and opportunities that applications leave for users.
This change will directly affect the business models of application developers. Tool-based applications are the first to be impacted, and the ecological value of transactional and social apps will also be shaken. Whether it's information flow advertising that relies on dwell time and exposure distribution, membership subscriptions that form a sticky user base through deep usage, or commission models that generate revenue through a closed-loop transactional chain, all will be affected to varying degrees.
The white paper estimates that if the penetration rate of intrusive agents on the user side reaches 25%, the commercial value of tool applications is expected to decline by 39%, that of content and social applications by 19.5%, and that of transactional applications by 15.4%. This means that intrusive agents do not bring about a single-point efficiency optimization but rather a redistribution of the commercial value of the application ecosystem.
User-initiated traffic redirection
Source: Analysis by Frost & Sullivan
PART.03
Traffic migration has not brought about significant increases but instead led to industry involution and high governance costs.
The white paper further points out that the impact of intrusive agents is not mainly reflected in new industry additions, but more so in the redivision of existing traffic distribution relationships. When system-level agents attempt to bypass existing cooperation mechanisms and directly handle user demands, the relationship between platform providers, application developers, and agent providers shifts from collaborative cooperation to a zero-sum game.
Intrusive Agent, Infiltrating and Crowding-in Tools
Source: Analysis by Frost & Sullivan
In this process, application developers often need to defend against unauthorized data reading and interface operations through methods such as interface adjustments, anti-automation recognition, tightened permissions, and policy updates. This has led to a simultaneous increase in software iteration frequency, compatibility maintenance difficulty, and security protection investment.
Research shows that when the penetration rate of intrusive agents reaches 25%, the comprehensive development cost of mobile applications is expected to rise by 16%, including costs for cooperative coordination, compliance review, and security defense, which are expected to increase by 34.4%. This trend indicates that without clear rule constraints, the intrusive development path may consume more industrial resources in confrontation rather than innovation itself.
PART.04
The centralized superposition of high-permission commands induces significant risks to data and asset security.
To achieve continuous task execution across applications and scenarios, intrusive agents typically require long-term possession of higher system privileges and maintain account login and operation capabilities across multiple business scenarios. This means that the data boundaries and permission boundaries, which were originally scattered across different applications, begin to converge towards a single agent. Once there is abuse of permissions, model misjudgment, or security gaps, the impact will no longer be limited to a single application but may spread to the entire terminal environment.
At the same time, since intrusive agents have the capability to read screen content and trigger subsequent actions, they can also be induced by external information. Attackers can embed specific instructions through carriers such as web pages, emails, and documents, inducing agents to perform sensitive operations such as deletion, forwarding, modification, and payment without the user's full awareness. Originally localized and controllable recognition biases can rapidly evolve into privacy leaks, account risks, or even asset losses in a highly privileged execution environment. It is evident that the intrusion of intrusive agents brings not only traditional information security issues but also a systemic risk amplification under the backdrop of centralized permissions.
Batch accidental deletion of files from the cloud disk is triggered by zero-click email instructions.
Source: Analysis by Frost & Sullivan
PART.05
Based on dual authorization and full-link auditability, a trusted governance framework is built.
In response to the aforementioned issues, the white paper proposes that the sustainable development of the Agent industry should not be based on crossing ecological boundaries and weakening trust mechanisms. Instead, it should promote the formation of a governance framework that primarily relies on API collaboration with GUI simulation as a supplement. The core lies in establishing a dual authorization mechanism, which means that when an Agent performs cross-application operations, it not only needs explicit authorization from the user for system permissions but also permission from the called application or service provider for specific business actions.
At the specific implementation level, a credible governance framework should be developed in four directions. First, clearly define the authority boundaries and scope of tasks that agents can handle to prevent capability spillover. Second, set stricter action constraints and confirmation mechanisms for high-risk operations involving privacy, payment, assets, and identity changes. Third, establish a full-process traceability system covering authorization, decision-making, execution, and result feedback to ensure that key operations are verifiable and reviewable. Fourth, further clarify responsibility attribution on an auditable basis to provide a basis for subsequent dispute resolution, loss determination, and institutional constraints.
The white paper ultimately emphasizes that the evolution direction of Agent technology should not be to replace collaboration with intrusion, but rather to achieve cross-entity collaboration within a verifiable, constrained, and accountable framework. Only on the premise of ensuring ecological order, business fairness, and user safety can Agents truly unleash their long-term value for improving social efficiency.
PART.06
Global competition cannot be at the expense of overrelying on trust. Trustworthy governance will become a prerequisite for China's AI industry to enter the international market.
The white paper further points out that we should not understand intrusive agents solely from an innovative perspective, but should consider their comprehensive impact on the industrial ecosystem, public privacy, security environment, and international competition. Agents are not just a competition in model capabilities, product forms, and deployment speed; they are also a competition in governance capabilities, trust foundations, and rule adaptation abilities. If intrusive agents take the risk of breaking through permission boundaries, weakening authorization mechanisms, and sacrificing user trust as a cost, they may seem to gain an early advantage in competition, but in reality, they could pose greater risks to the development of the entire industry and society.
The evolution of the competition landscape between China and the US in AI is not only related to technical capabilities and implementation speed but also increasingly depends on whether sustainable balance can be achieved among innovation efficiency, security constraints, and ecological collaboration. For China's artificial intelligence industry, an Agent system with a foundation of security, trust, and rule compatibility will more effectively enhance its long-term competitiveness in the global market; if it overly relies on an expansion path that sacrifices trust, it may not only weaken the international cooperation space for individual products or enterprises but also have a negative impact on China's overall international reputation. It also poses a negative obstacle to integrating into the global mainstream AI technology routes and governance systems.
