In the first quarter of 2025, under the backdrop of tightening cybersecurity regulatory frameworks in China, the Aliyun Shadow Team announced plans to launch two hardware terminals, JVS Book and JVS Box, based on an OpenClaw-like architecture, focusing on offensive and defensive as well as security scenarios. This move resonates with the OpenClaw Security Usage Guidelines previously released by the Chinese Cyberspace Administration: on one side, the regulations clearly delineate boundaries for offensive and defensive open-source AI, while on the other side, industrial forces are accelerating the productization and terminalization of similar capabilities. The real core contradiction surrounding tools like OpenClaw is surfacing — how can open-source offensive and defensive AI achieve large-scale implementation under the tight regulations, and this tension is being amplified.

Open-source offensive and defensive tools go mainstream: From community to major enterprises

OpenClaw initially gained popularity in the technical community as an open-source offensive and defensive tool, quickly attracting the attention of security researchers and red-blue teams through its open code and reproducible offensive and defensive capabilities. As offensive and defensive scenarios are continuously restructured with the support of AI capabilities, OpenClaw has evolved from a niche security topic into a symbol frequently mentioned by mainstream technology media and industry practitioners — it represents not only more efficient penetration simulation and defense drills but also the risks of continuously challenging regulatory and ethical boundaries.

During this evolution process, the Aliyun Shadow Team chose to introduce an OpenClaw-like architecture and extend it to hardware terminals like JVS Book and JVS Box, with a clear intention: to encapsulate the originally highly engineered and professionally deployed offensive and defensive capabilities into standardized terminals, directly targeting enterprise security departments, government agencies, and frontline operators. The hardware and terminalization mean moving from "tools are for geeks" to "devices are for organizations", also reserving interfaces for future access, certification, and operational audits.

Meanwhile, the AI sector's trending "shrimp farming" agent track provides an intuitive vision for the combination of offense, defense, and automation. According to information from a single source, “shrimp farming” models that accomplish high-frequency tasks through a large number of AI agents working in coordination have already become one of the more highly focused directions. If we view tools like OpenClaw as the intensive encapsulation of offensive and defensive capabilities, and the "shrimp farming" agents as amplifiers for automated execution and operations, their overlap signifies: offense and defense no longer constitutes intermittent drills but may evolve into normalized, automated, and even adaptive security operation networks.

Regulatory restraints take effect: Security guidelines are ever-present

The release of the OpenClaw Security Usage Guidelines by the Chinese Cyberspace Administration serves as a key anchor point in this wave of regulation surrounding offensive and defensive AI. The backdrop for the guideline’s introduction is the rapid dissemination of open-source offensive and defensive tools among domestic developers and enterprises, with their replicability and diffusion speed far exceeding traditional security tools, which enhances the quality of defensive drills while magnifying the potential risks of malicious misuse. The core concerns of regulatory agencies focus on three levels: whether the offensive capability will overflow to uncontrollable groups, whether data and models will be used for illegal penetration, and how to delineate the responsibility boundaries when enterprises self-develop or use such tools.

From publicly available information, we can infer that this guideline's constraints on open-source offensive and defensive tools mainly reflect on compliance usage scenarios, authority boundaries, and responsibility divisions. First, it explicitly requires that offensive and defensive testing be conducted within the scope of legal authorization, prohibiting the use of OpenClaw-like capabilities for unauthorized targets' penetration and scanning. Second, it puts forward higher requirements for identity management, log records, and operational traces regarding the users of these tools, preventing the gray area of “tool sharing, responsibility disappearance”. Third, it guides organizations to establish accountable internal compliance processes when procuring, deploying, and further developing such tools.

Under the concept of “controllable and usable”, the security guidelines outline red lines: offensive and defensive capabilities must not be commoditized into public-facing “black box attack services”, and large-scale penetration capabilities must not circumvent real-name and audit mechanisms; on the other hand, it also leaves gray areas: enterprises can deeply customize OpenClaw-like solutions within their own networks and controlled environments, as long as they are traceable, accountable, and can be shut down. It is in this space between red lines and gray areas that vendors begin to seek their own product and architectural placements, striving to avoid being labeled as “risk tools” while also seeking to occupy the entry points of the next generation of security infrastructure.

Aliyun bets on JVS terminals: Accelerating charge under the regulatory spell

Within this regulatory framework, the Aliyun Shadow Team bets on JVS Book and JVS Box, positioning them as dedicated terminals for offensive and defensive security scenarios, carrying distinct strategic significance. On one hand, hardware terminals are naturally easier to integrate into existing security review and procurement systems, capable of connecting with traditional infrastructures like data centers, government clouds, and enterprise SOCs; on the other hand, this form also provides a physical boundary for “clouding offensive and defensive capabilities”, helping to alleviate regulatory concerns about capability overflow and misuse.

After the security guidelines took effect, large firms not only did not withdraw but further committed to OpenClaw-like solutions, backed by a nuanced game mentality and pragmatic risk management demands. On one hand, large firms hope to establish credibility before regulators through “proactive compliance + self-built standards”, seeking more room for capability development; on the other hand, they also realize that if they completely withdraw from the open-source offensive and defensive AI landscape, the future discourse power over security infrastructure may be seized by more radical players. Thus, integrating offensive and defensive capabilities under the compliance red line in the form of products and terminals becomes a compromise choice.

From a business perspective, the potential demand that Aliyun can connect in enterprise markets and government compliance scenarios is clearly visible: large government and enterprise clients need an auditable, centrally controllable offensive and defensive platform, rather than scattered scripts and individual tools; key industries like finance, energy, and communication place greater emphasis on the normalization of offensive and defensive drills and automated responses, hoping to achieve upgrades in security operations under controllable cost conditions. Around the JVS series, the business model that Aliyun can build would tend more toward a combination of “hardware terminals + cloud control + service subscription”, locking in high-value clients with project-based delivery, long-term operations, and compliance support.

Global risk sentiment intensifies: Offensive and defensive AI leverage the momentum

On a more macro level, the global risk sentiment itself provides fertile narrative soil for the expansion of offensive and defensive AI. Research briefs indicate that the turmoil in the Middle East is seen as one reason for the sharp fluctuations in the precious metal market (specific price data awaiting verification or from a single source, requiring cautious interpretation); this geopolitical uncertainty is reinforcing enterprises' and institutions' overall demand for “security” and “hedging”. At the same time, concerns about rising oil prices potentially impacting Asian economic growth (according to a single source) are also fermenting, and under an environment of compounded cost pressures and growth uncertainties, enterprises are more inclined to choose cost-controllable, scalable security automation tools, rather than point solutions that rely heavily on human input.

Changes in funding preferences are also becoming evident. The research brief notes that assets held by five major exchanges in South Korea have shrunk by about 55% since July 2025 (according to a single source). Although there is a lack of more detailed time series and platform breakdown data, this significant shrinkage itself suggests a tightening and repricing process of regional risk preferences. As risky assets are reduced, assets and tools with more compliance attributes, safety attributes or “hedging” narratives often receive relative premiums. In this structural repricing cycle, offensive and defensive AI and security automation products that emphasize “strengthening defenses and reducing tail risks of security incidents” will naturally receive more budget and management attention.

In the atmosphere of rising global security and economic uncertainty, OpenClaw-like tools and their commercialized derivative forms are no longer just “cool toys” within the tech circle but are being examined within larger governance and asset allocation contexts: Can they reduce asset losses caused by security incidents? Can they find optimal solutions between regulatory requirements and budget constraints? These questions are driving enterprises and institutions to reassess the structure of security investments, reallocating some funds from simple firewalls and antivirus software to smarter, more automated offensive and defensive platforms.

From “shrimp farming” to industrial-level offense and defense: The convergence of agents and security operations

The “shrimp farming” agents, as a trending track within the AI circle, are seen as a concentrated experiment in high-frequency automation and complex task scheduling. By deploying a large number of AI agents working collaboratively, continuously rolling out data capture, strategy execution, content generation, etc., this model reflects the industry's strong interest in “unmanned, high concurrency, self-adjusting” systems. In the security field, such paradigms naturally possess attractiveness — because true offense and defense is not a one-time task but an enduring battle 24/7.

When juxtaposing OpenClaw-like tools with “shrimp farming” agents, we can see the attempts at offensive and defensive capabilities and automated operations are converging toward the center. On one end is the highly specialized offensive and defensive engine, capable of simulating attacker thought processes and identifying system vulnerabilities; on the other end are agent clusters with task orchestration and long-cycle execution capabilities, which can persistently inspect, repair, and return to validation when issues are identified. When these two technological lines are integrated within an enterprise, a “self-driven security operation entity” begins to take shape: it can use OpenClaw-like tools as weapons while employing “shrimp farming” agents as the limbs.

At the capital and entrepreneur level, this intersection also means a new channel for elevating expectations: offensive and defensive AI, security operation agents, and automated operations are bundled under the broader theme of “security infrastructure upgrades”. Funds need not bet everything on a single track but can invest in mutually beneficial narrative combinations; entrepreneurs can find a foothold across different slices such as offensive and defensive capabilities, agent orchestration, and visualized operation platforms. The outcome is that a segment originally viewed as niche and technical may be nudged toward a larger industrial stage under the resonance of macro risks, regulatory changes, and advancements in technological paradigms.

Regulation and business race: The long-term game of OpenClaw

In summary, under the framework constraints of the OpenClaw Security Usage Guidelines, OpenClaw-like solutions are evolving along two simultaneous paths: one remains rooted in the open-source community, continuing to iterate capability boundaries in tool form; the other is transitioning toward standardization, compliance, and commercialization through enterprise terminals like JVS Book and JVS Box. The former maintains innovation speed and technological sharpness, while the latter assumes the role of interfacing with regulations, compliance, and industry standards, with tension existing between the two for a long time.

The broader game is a triangular interaction of regulation, industry, and global macro risks: regulators hope to bring offensive and defensive AI under visible and manageable trajectories; industries hope to release commercial value as much as possible without being slowed down by the label of “security”; while macro risks and geopolitical turmoil continuously remind the market — security is not optional but a pricing factor equally important as returns. The offensive and defensive AI tools represented by OpenClaw will continuously reshape their commercial logic and technical boundaries in this long-term game.

Looking forward, the key to success in this track lies not in who has the flashier attack posture or more model parameters, but rather in how to truly productize offensive and defensive capabilities within clear compliance boundaries and earn the dual trust of the market and regulators. Whoever can build a “auditable, measurable, and trustworthy” security AI system first will likely occupy a high ground in the next round of security infrastructure reconstruction. For OpenClaw and its similar solutions, the real charge has only just begun.

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