Villager: the AI Pentest tool that disrupts and worries cybersecurity experts

In a context where mastering cyberattacks is becoming an absolute priority for businesses and governments, the arrival of a new tool powered by artificial intelligence is making a big splash: Villager. Designed to automate penetration testing, this program revolutionizes the world of cybersecurity, combining power, speed, and efficiency. Yet, this technological innovation raises as many hopes as concerns. Indeed, its widespread use could exacerbate the threat faced by critical infrastructures worldwide. Nearly 62% of currently automated penetration tests use AI pentesting, demonstrating a genuine shift in vulnerability auditing methods. But the rise of Villager also reminds us that automation fuels a new era where the line between ethical hacking and malicious exploitation dangerously blurs. This duality questions the ability of cybersecurity players to keep pace with these AI tools with superhuman performance.

Since its appearance on PyPI, Villager has been downloaded nearly 11,000 times, illustrating the growing demand for intelligent solutions capable of orchestrating complex offensive campaigns. Developed by the Chinese group Cyberspike and named “stupidfish001” by its initial author, this agent uses natural language processing to carry out the various attack phases, from reconnaissance to post-exploitation. This complete automation transforms the very nature of penetration tests, forcing security teams to rethink their traditional approaches. However, the existence of such an accessible tool raises major questions: how to regulate its use? What response to coordinated and rapid attacks where AI neutralizes vulnerabilities before they can be detected? Our detailed exploration of Villager reveals the mechanisms of this emerging technology, its ramifications in the field of AI pentesting, as well as the challenges it imposes on cybersecurity actors in 2025.

What is Villager: Understanding the AI Tool Shaking up Traditional Pentesting

Villager presents itself as an intelligent offensive agent developed in the shadows by Cyberspike, a group based in China that has already sparked controversies in the past. Unlike traditional pentesting tools, it is not simply a vulnerability exploitation software. It is a true AI platform capable of fully automating the entire intrusion process.

This software is distributed as a Python module on PyPI, which facilitates its access and integration into different environments. Users can send natural language instructions to Villager, which interprets them thanks to its engine based on advanced natural language processing (NLP) models. It deduces the necessary technical actions, orchestrating each step of the offensive campaign: from information gathering to compromise, including backdoor installation and persistence maintenance on targeted systems.

The significance of this innovation is even more remarkable as Villager positions itself as a direct successor to well-known tools such as Cobalt Strike, long used by ethical hacking teams and legitimate pentesters. Where Cobalt Strike required strong human expertise to efficiently exploit each feature, Villager pushes automation to an unprecedented level, offering the possibility to fully execute a penetration test without continuous human intervention.

The consequences are significant: nearly 62% of automated penetration tests in 2025 already use AI, a trend driven by the demonstrated efficiency of such agents. But this progress does not come without raising ethical and security questions, because Villager could serve both defensive purposes and malicious ends. Its mode of operation, based on agentic AI, makes it capable of making tactical decisions in real time, far beyond the mere execution of predesigned scenarios.

In summary, Villager is not just a simple pentesting tool. It is an intelligent platform that disrupts the way intrusion campaigns can be conducted, offering a strategic advantage to both defenders and attackers. As its use continues to grow, the entire cybersecurity community questions the measures to adopt to control this emerging power.

The Technical Architecture of Villager: A Leap Forward for Cyberattack Automation

The internal operation of Villager relies on a sophisticated combination of modern technologies. The core system uses AI models from DeepSeek AI, enabling dynamic and evolving exploit generation. Thanks to this foundation, the agent can adapt its attacks in real time by varying payloads, giving it a unique ability to bypass usual protections.

The structure also includes full integration with reference pentest tools, notably Kali Linux. Each intrusion phase is orchestrated through a central command and control (C2) layer built on FastAPI, ensuring smooth and secure communication between the human operator and the AI agent. This setup also facilitates result analysis, standardized by the AI agent Pydantic, allowing quick and consistent interpretation of data collected during the attack.

A key aspect of this architecture is the library of over 4,200 contextualized AI prompts. These prompts feed the agent with precise scenarios adapted to the attack context, thus increasing its chances of success. The system does not merely execute commands once: it repeats attempts upon failure by slightly modifying parameters, ensuring exceptional persistence. This self-adaptation capability makes Villager a tool far surpassing the classic capabilities of audit tools.

The use of the Model Context Protocol serves as a bridge between the user interface and the machine, transforming natural language instructions into technically precise actions. This redefines the role of the human operator, now responsible for overall strategy, while the AI agent takes charge of tactics at an extremely detailed and rapid execution level.

This complex and highly automated architecture represents a significant advance in the field of cybersecurity testing. It enables a level of efficiency never before achieved but also raises strong concerns about the difficulty of countering or even tracing such attacks on critical systems.

Enhanced Stealth via Programmed Destruction: Ephemeral Hacking in the AI Era

Beyond its offensive capabilities, Villager also innovates in stealth. The tool implements a so-called “ephemeral hacking” method that greatly complicates post-incident analysis and cyber defense operations.

Concretely, each attack phase is carried out in isolated Kali Linux containers whose lifespan is strictly limited to 24 hours. These environments are activated for each scan and immediately destroyed once the window expires, thus removing traditional traces that analysts could exploit. Furthermore, the use of random SSH ports makes network communication tracking even more challenging.

This automated destruction of the offensive infrastructure means that intrusion evidence almost instantly disappears, forcing security teams to abandon artifact-based approaches. They must now focus on real-time behavioral detection, which spots anomalies based on activity patterns rather than static data.

In practice, this strategy significantly increases the operational load on SOC monitoring centers, which must adopt flow analysis tools and strengthen their investigation capacities on suspicious behaviors. Attacks via Villager thus become less traceable and more complex to attribute, notably due to the elimination of human footprints resulting from the complete automation of the process.

The situation becomes even more complicated considering the AI used in Villager can generate deepfake audio or text to support phishing campaigns, further muddying the trail. Some experts even mention the possibility of multidimensional attacks, combining technical attack models with disinformation operations, making response efforts even more arduous.

This aspect of accelerated and stealthy hacking clearly explains why Villager alarms the international community, with regulators at the forefront fearing the impact of these attacks on critical services such as health, energy, or public transport.

The Speed and Efficiency of Villager: A New Era in Vulnerability Management

One of Villager’s major contributions lies in its ultra-fast execution. Where human hackers or even sophisticated teams take days or weeks to carry out complex operations, Villager can perform thousands of requests per second. This superhuman pace radically changes the balance of power.

Thanks to this speed, the delay between vulnerability discovery and exploitation is reduced by 20 to 25 times on average. In simulations, Villager’s performance has shown it can shorten the duration of a full security audit by 90-95%. This rapidity forces AI pentesting and cybersecurity specialists to adopt continuous auditing strategies.

This advanced automation not only allows faster detection of flaws but also enables chaining of multi-vector attacks, simulating campaigns conducted by state actors or APT groups. This phenomenon alarms SOCs observing an increase in incidents involving coordinated and sophisticated attacks, primarily targeting critical infrastructures.

Beyond the immediate impact on operational security, the use of tools like Villager also broadens the spectrum of actors capable of carrying out complex attacks previously reserved to highly qualified experts. The democratization of these technologies thus amplifies the threat.

In response, many organizations have already integrated AI tools into their defensive processes: in 2024, 75% of security teams reported using AI-assisted solutions. This technological race between defenders and attackers well illustrates the rise of modern cybersecurity, based on speed and real-time anticipation.

The Limits of AI in Pentesting: When Human Intuition Remains Indispensable

Despite all its sophistication, Villager remains subject to its own limits. Artificial intelligence excels particularly in managing repetitive tasks and systematically searching for known vulnerability patterns. Yet, it struggles with deep flaws tied to specific business logics and unforeseen scenarios.

The typical flaw escaping AI is often rooted in the unique architectural design of a system, not corresponding to a standard model. For example, exploitation cases related to complex manipulation of business rules, or bypassing automated approvals, remain difficult to detect without human intuition and expertise.

Studies show that about 85 to 90% of these very specific vulnerabilities are detected by experienced human testers, compared to only 50 to 65% for AI systems like Villager. This gap highlights that the complementary relationship between human and machine constitutes the best defense, AI speeding diagnostics, and humans refining through semantic and contextual reasoning.

It is this hybridization between machine and operator that will ultimately allow fully leveraging AI pentesting’s potential while minimizing risks linked to uncontrolled automation. Villager, despite its technological advance, thus illustrates the permanent necessity of human expertise in cybersecurity.

The Regulatory Framework Facing Villager: A Troubling Legal Void

The rise of dual-use tools like Villager reveals the absence of a clear and harmonized regulatory framework at the international level. While the US executive order recognizes these technologies as “foundational dual-use models,” the European Union chooses not to use this term, creating a damaging legal grey area.

Controlling this class of tools, combining data, algorithms, and computing power, remains a challenge for authorities. In France, a national law aligned with the European AI Act has imposed since 2025 strict requirements on so-called high-risk systems, notably regarding transparency, traceability, and risk management. These measures aim to regulate generative models and limit malicious uses.

However, assigning responsibilities remains complex. Tort law struggles to apply to an autonomous AI system whose behavior is not reproducible and difficult to anticipate. The debate around qualifying advanced AI as a “product” in the legal sense also contributes to maintaining this insecurity.

In this context, the lack of clear sanctions risks encouraging some entities to push autonomy and automation even further, which could worsen already considerable threats. This legal grey zone calls for enhanced international cooperation to develop appropriate policies capable of effectively containing this new arsenal.

An Evolution Towards Collaborative and Collective Cybersecurity

Facing the challenges posed by Villager and its contemporaries, the cybersecurity community tends to move towards more collaborative models. Secure and anonymized sharing of information derived from attacks and new AI tactics can constitute a strategic bulwark.

In France, where since 2025 more than 179 startups and 46 scale-ups are active in the cybersecurity sector, this dynamic manifests as the emergence of mutualized threat intelligence platforms. These initiatives enable teams to strengthen their vigilance and speed up reaction capabilities by sharing in real time information from tools like Villager deployed in various contexts.

This pooling facilitates the creation of an expert community capable of anticipating future trends and developing adapted defense strategies, combining the algorithmic power of AIs with collective human experience. In November 2025, SAP announced strategic partnerships with Capgemini, Bleu, and Mistral AI aiming to strengthen the French artificial intelligence ecosystem, highlighting this integrated approach.

This alliance between public, private, and institutional actors reflects the awareness that no defense can suffice on its own against a threat that automates and complicates itself worldwide. Balancing technological innovation and responsible regulation remains key to ensuring a safer digital future.

Comparative Table: Villager versus Traditional Pentest Tools

Criterion	Villager (AI Pentesting)	Traditional Tools (e.g., Cobalt Strike)
Automation	Full automation with agentic AI capable of making real-time tactical decisions	Partial automation, strong dependence on human expertise for execution
Execution Speed	Superhuman execution: thousands of requests/second, tests 90-95% faster	Limited by human capacity and manual speed
Stealth	Ephemeral hacking with programmed destruction of containers within 24 hours	More persistent infrastructure, facilitated post-mortem analysis
Adaptability	Base of 4,200+ prompts, automatic retries upon failure	Static scenarios, limited adaptation
Usage	Both ethical pentesting and potentially malicious (opaque use)	Often used in legitimate red teaming frameworks

Major Impacts of Villager on Cybersecurity in 2025

• Democratization of complex attacks: Villager allows a larger number of actors to execute sophisticated offensive campaigns.
• Shortening of vulnerability discovery delays, accelerating the fight against flaws and the race against time for defensive teams.
• Increased complexity of detection and attribution due to enhanced stealth and absence of human traces.
• Increased pressure on SOC teams to shift towards behavioral tools and continuous security audits.
• Urgent need for adapted regulatory frameworks to govern the use of dual-use tools and ensure sustainable cybersecurity.
• Emergence of international collaboration as a key vector for sharing information and slowing threat propagation.

What is the Villager tool in cybersecurity?

Villager is an AI-powered pentesting software capable of advanced automation of intrusion tests, developed by the Chinese group Cyberspike.

Why does Villager scare cybersecurity experts?

Because it fully automates cyberattacks with speed and stealth that are hard to detect, increasing the risk for critical infrastructures.

What is the main difference between Villager and classic tools like Cobalt Strike?

Villager uses artificial intelligence to make real-time tactical decisions and execute attacks automatically, unlike traditional tools that require strong human intervention.

How to secure environments against this new generation of AI tools?

It is crucial to strengthen real-time behavioral detection, adopt continuous security audits, and promote collaboration and information sharing among actors.

Is the human role definitively replaced by AI in intrusion tests?

No, complex vulnerabilities linked to business logic and unforeseen scenarios still require human intuition and expertise, complementing AI capabilities.