Attorney Shao Shiwei | Legal Red Lines and Compliance Survival Guide for GEO Service Providers

Analysis of Potential Legal Risks in the GEO Business Model

1. Why Data Feeding Easily Triggers Liability for Unfair Competition

In the practice of GEO services, "data feeding" is often regarded as a normal content enhancement strategy. Many teams' programmers, content operators, and data engineers use structured content deployment to increase brand visibility in public corpora pools. However, when the fed content is not based on true information but rather involves forging expert identities, fabricating industry reports, creating non-existent user reviews, or constructing "pseudo-authoritative webpages" through link farms to "shape advantages," its nature no longer remains within the scope of marketing but begins to touch the regulatory bottom line of the Anti-Unfair Competition Law.

According to Articles 9 and 12 of the Anti-Unfair Competition Law, business operators shall not engage in false publicity, nor fabricate or disseminate misleading information. This means that as long as the content fed by GEO service providers is sufficient to cause the public to form erroneous perceptions regarding a company's qualifications, technical capabilities, user reputation, etc., it may be deemed false publicity. Furthermore, if the relevant content improperly disparages competitors, implying they lack capability or are in a marginal state, it may also constitute damage to their commercial reputation.

In the GEO scenario, the risk increases significantly for two key reasons. First, feeding behavior is often systematic. Many GEO teams' programmers, algorithm engineers, and development engineers use automated methods to batch-generate structured content, causing it to spread continuously across different platforms. Eventually, this content is recorded by models into retrieval corpora or generation reference chains, leading to an "amplification effect" of false information. Second, AI responses carry a high sense of authority. When biased content is repeatedly cited by models, users may regard it as reliable fact, exacerbating the consequences of misinformation. Therefore, when assessing the legal nature of GEO activities, regulatory authorities emphasize their overall impact on the information ecosystem and market order, rather than merely examining whether the content itself is strictly true.

Essentially, once data feeding crosses the boundary of truthfulness, it may evolve into creating an "information illusion" through technical means. Such behavior disrupts fair competition based on true information and reduces the normal visibility of market entities. Therefore, even if GEO service providers do not directly participate in transactions or publicity, as long as their fed content is used to influence AI responses and thereby affect consumer judgments, they will face legal risks related to false publicity or misleading information.

In currently public enforcement cases, cases specifically involving GEO are still rare. However, cases related to SEO have highly transferable reference value for GEO. The penalty imposed by the Changning District Market Supervision Administration of Shanghai in 2024 in the "Hotel Brother" confusion case is an important sample for understanding such risks.

[Typical Case]

Extension of GEO Feeding Risks from the Perspective of SEO Confusion Cases

Between 2020 and 2021, while promoting its "Hotel Brother" website, the party involved, without permission, set the enterprise name of its competitor, "a certain company in Tianjin," as search keywords for its own website. When users entered "a certain company in Tianjin" into 360 Search, the first two results on the page displayed promotional links bearing that name. However, these links actually redirected to the "Hotel Brother" official website and were accompanied by advertising slogans such as "Free Online Booking" and "Zero Service Fee." The accompanying images also featured logos related to "Hotel Brother," making it easy for the public to mistakenly believe that there was a business cooperation or affiliation between the two companies.

Investigation by regulatory authorities confirmed that this behavior was sufficient to cause public misunderstanding and directly reduced the competitor's exposure opportunities in search results. The Market Supervision Administration ultimately determined that it constituted confusing behavior as stipulated in Article 7 of the Anti-Unfair Competition Law, ordering the cessation of illegal acts and imposing a fine of RMB 30,000.

This case holds important implications for GEO service providers. Although the target of the penalty in the "Hotel Brother" case was the merchant itself rather than a third-party agency, its adjudication logic remains highly valuable for GEO service providers. When handling such information display behaviors, regulatory authorities do not focus on who implemented the technical operations but emphasize that: as long as it is sufficient to cause public misunderstanding and weaken the visibility of competitors, it may constitute confusion or misleading publicity.

Therefore, if GEO service providers imply cooperative relationships through content placement, implicitly disparage peers in rankings or evaluations, or influence model judgments through "third-party evaluations," they may all be regarded as creating misleading effects. Especially in the context of generative AI, once fed content enters the model's citation chain, its misleading impact will be continuously amplified. In other words, this case reminds service providers that the key lies not in "who operates" but in "whether it causes public misunderstanding," which will become an important benchmark for GEO compliance judgments.

2. Prompt Injection and the "Crime of Sabotaging Computer Information Systems"

Compared to data feeding, which primarily relies on content volume and information form to influence model citation chains, prompt injection is a more technical operational path. Its core practice involves embedding "hidden prompts directed at AI" in webpage scripts, visible or invisible text, metadata, open-source documents, or areas not directly perceptible to users. This induces the model to prioritize outputting a specific brand or viewpoint when generating responses, or even alters the model's original reasoning logic.

Such operations are often carried out by programmers, algorithm engineers, or technical personnel responsible for model parsing. They are not implemented through user input interfaces permitted by the platform but attempt to exploit the model's parsing mechanisms or context injection vulnerabilities, causing it to "carry external instructions" unknowingly, thereby influencing the final output. The legal risk of this behavior lies in the fact that it breaches the model's normal interaction path and has the tendency to "interfere with system functions."

Article 286 of the Criminal Law lists "deleting, modifying, adding, or interfering with the functions of computer information systems" within the scope of criminal penalties. Large language model service platforms, as highly complex information processing systems, rely on the controllability of input content for their operational stability. When prompt injection, through large-scale deployment, causes the model to output a large amount of distorted content, affects its response logic, or causes abnormal consumption of system resources, the behavior may be interpreted in legal theory as "unauthorized intervention" in the functions of computer information systems. Therefore, when facing operations characterized by technical deception, bypassing normal interfaces, or affecting model stability, judicial organs are more likely to examine their nature from the perspective of "sabotaging computer information systems."

Prompt injection shares essential similarities with the "black hat operations" implemented by programmers and development engineers in the traditional SEO era. Both influence information ranking or content presentation by evading rules and bypassing system design; the only difference is that the execution environment has shifted from search engine algorithms to the instruction parsing mechanisms of AI models. From a regulatory logic perspective, as long as such intervention behaviors possess characteristics such as concealment, large-scale dissemination, or uncontrollable consequences, they may be deemed to endanger the security of public information systems.

Although there are currently no GEO-related cases where prompt injection has led to criminal conviction, relevant criminal cases in the SEO field have provided insights into the judgment logic of judicial organs.

[Typical Case]

Interfering with Search Engine Rankings, Sentenced to One Year and Six Months Imprisonment

In 2023, the Yushan District People's Procuratorate in Ma'anshan City, Anhui Province, publicly disclosed a case in which the defendant, Li Mou, was convicted of the crime of providing programs and tools for intruding into or illegally controlling computer information systems for developing and selling tools such as "Spider Luring Programs," and was sentenced to one year and six months imprisonment.

According to public records [2], Li Mou acquired programming skills through self-study and developed programs capable of simulating abnormal access and interfering with the normal ranking of search engines, which he sold to multiple users. Additionally, he provided services such as software upgrades, Cookie channels, and CAPTCHA solving to ensure the programs could continuously bypass the protective mechanisms of search engines. Such programs generated a large amount of abnormal traffic to search engine servers in a short period, with some links pointing to gambling or pornographic websites, directly affecting the stable operation of the search engines.

The court ultimately determined that the tools provided by Li Mou possessed obvious attributes of "interfering with the functions of computer information systems," and his behavior had transcended the general sense of "marketing tools," constituting an infringement on the security of public information systems.

In recent years, courts nationwide have tried multiple cases involving "Black Hat SEO." The core focus of judicial organs is not "whether it is used for marketing," but rather:

• Whether it breaks through the preset interaction methods of the system;

• Whether it utilizes vulnerabilities or technical means to bypass normal rules;

• Whether it poses a risk of interference to system operation;

• Whether it has characteristics of large-scale dissemination or uncontrollable consequences;

• Whether it possesses attributes of being tool-based, replicable, and diffusible.

When the above factors overlap, even if the behavior itself has commercial purposes, it may be recognized as a technical intervention behavior endangering the security of computer information systems.

In the GEO scenario, if prompt injection tools are used to manipulate model outputs on a large scale, or cause the model to make systematically biased responses toward specific brands, their operational logic is essentially no different from typical black hat programs. Therefore, there is a realistic possibility that such behavior will be reviewed by judicial organs under the framework of the "Crime of Sabotaging Computer Information Systems" or the "Crime of Providing Programs and Tools for Intruding into or Illegally Controlling Computer Information Systems."

Compliance is the Core Barrier for GEO Service Providers

1. GEO Service Providers are Moving Towards "Regulatability"

With the rapid development of generative AI, the regulatory framework is gradually shifting from principled norms to systematic governance. The most core aspect is the requirement for entities providing deep synthesis, generative content, or algorithmic recommendation services to undergo filing, so that regulatory authorities can conduct traceability reviews of algorithm mechanisms, data sources, and content generation modes when necessary.

For GEO service providers, this trend means that the industry will gradually shift its legal positioning from "technical services" to "public information services." Especially when service content involves influencing model responses, structured supply of corpora, and construction of industry knowledge bases, related operations may fall within the scope of application of the "Provisions on the Administration of Deep Synthesis of Internet Information Services," the "Measures for the Labeling of AI-Generated and Synthesized Content," and the "Provisions on the Administration of Algorithmic Recommendations for Internet Information Services." These regulations emphasize:

• Only those providing deep synthesis or algorithmic recommendation services with "public opinion attributes or social mobilization capabilities" are required to fulfill filing obligations;

• Once an algorithm falls within this scope, it must undergo stricter filing review and report to regulatory authorities during changes, upgrades, or termination.

For GEO service providers, this implies two important changes:

First, filing is no longer an "optional item" but a mandatory threshold triggered by specific conditions.

Filing is mandatory only when regulatory authorities determine that the technical means possess "public opinion attributes or social mobilization capabilities." Before this condition is triggered, enterprises may file voluntarily, but once triggered, failure to file constitutes a compliance risk.

Second, in heavily regulated industries (such as healthcare, finance, education, etc.), "having completed filing" is becoming an implicit condition for market supplier selection.

To hedge against AI usage norms and content safety responsibilities, clients are increasingly inclined to choose service providers that have completed filing, have transparent technical architectures, and are open to audits. Filing status is thus transforming from a "compliance cost" into a "competitive advantage."

In short, the basic competitiveness of the future GEO industry lies not only in "whether optimization is possible" but also in "whether optimization can be conducted transparently within the regulatory framework."

Teams that can establish algorithm archives, data source explanations, and internal security processes first will occupy a first-mover advantage in the standardization process.

2. Content Transparency and Industry Verticalization: A Realistic Path to Building Long-Term Competitiveness

Against the backdrop of increasingly strict regulation, GEO service providers face a core question: How to continue providing effective visibility enhancement services for clients without touching legal boundaries such as false publicity, misleading information, or system interference? The answer often lies not in technical "breakthroughs" but in the "deepening" of content and industry expertise.

(1) Establishing a Transparent Content System: Making the Optimization Process Explainable and Auditable

For regulators and clients, "black-box optimization" is no longer an acceptable service model. The market is proposing new content standards for GEO service providers:

First, content sources must be transparent.
Whether it is industry data, expert opinions, or user reviews, all must have authentic sources, avoiding fabrication, invention, or semantic disguise.

Second, content logic must be explainable.
Clients want to know why a certain viewpoint, semantic tag, or structured field is constructed in a particular way, and models also prefer content with clear structure and consistent semantics.

Third, optimization effects must be quantifiable.
Providing data such as keyword-level exposure logs, model response citation records, and content indexing paths will become a basic market requirement for GEO service providers.

Content transparency is not only a compliance requirement but also significantly enhances the credibility of service providers in communication with clients, avoiding entanglement in risk disputes arising from the clients' own businesses.

(2) Deepening Vertical Fields: Shifting from "Keyword Optimization" to "Industry Knowledge Supply"

As governance rules of AI platforms continue to tighten, ordinary content stacking and generalized corpus diffusion can no longer achieve significant results and may even be downweighted by models due to repetition or low quality. A more sustainable trend is emerging in the industry—Verticalized GEO。

The key to verticalized GEO lies in:
No longer attempting to influence the model through quantity, but influencing model judgments through "professional consistency" and "knowledge credibility."

This is mainly manifested in three directions:

First, constructing industry knowledge bases.
 In heavily regulated industries such as healthcare, finance, and education, models are more sensitive to professional knowledge. If service providers can construct professional knowledge bases based on authentic materials and present them in a structured manner, models are more likely to adopt them as stable reference sources.

Second, establishing professional content templates.
AI prefers content with stable logic and consistent structure. If service providers can form industry-standardized templates, they can increase the model citation rate of client content without violating regulations.

Third, strengthening domain authority rather than simply stacking content volume.
Models tend to cite professional sources that are updated stably over the long term, rather than large volumes of corpora lacking logic and professional depth. This means that the core value of service providers is shifting from "producing content" to "providing professional knowledge structures."

In other words, future GEO services will no longer be a "traffic business" but a "professional supply chain," whose value lies in reconstructing industry knowledge in a form absorbable by models, rather than deploying massive amounts of indiscriminate content.

Conclusion

Under the general trend of continuous improvement in regulatory frameworks and strengthened content safety governance by AI platforms, GEO service providers are transforming from "participants in technical gray areas" to "participants in the information ecosystem." Algorithm filing, content transparency, and industry specialization will become the three main lines for industry survival.
 Only teams that can establish methodologies within norms and form stable semantic supplies through professionalism can truly establish their industry status.