AI Bubble Politics? Or A Preview To The Future

Anthropic Provides a Possible Glimpse Into The Future of Security Testing

Anthropic has released an interesting report in mid November 2025. The company claims it has identified an attack chain attributed to a threat actor associated with the Chinese State, which managed to execute a full blown cyberattack using Claude Code AI integrated with penetration testing tools. Although there’s no response to these accusations from China. Anthropic’s evaluation identified that the AI agent handled 80-90% of the cyber attack’s operations, and executed an almost perfect cyber kill-chain. This threat actor targeted roughly 30 entities such as big tech companies, financial institutions, chemical manufacturers, and government agencies across multiple countries. The first instance of an AI driven threat actor is a very bold claim, and in this blog we’ll uncover some facts underneath this case study done by Anthropic.

Anthropic reports first ever cyber attack by AI

On 13 November 2025, Anthropic released a blog and a full report titled ‘Disrupting the first reported AI-orchestrated cyber espionage campaign’. In this report, they claim that in mid-September 2025, they detected suspicious activities that later turned out to be highly sophisticated cyber-attacks using penetration testing tools integrated with the Claude Code application to target around 30 businesses and organizations across multiple countries. After detecting the threat actors, an investigation was launched to determine the scope and severity, Anthropic banned the associated accounts, contacted affected entities, and collaborated with appropriate authorities to gather threat intelligence. Anthropic also claims to have associated this cyber attack with the known threat actor designated as GTG-1002.

What is notable though, this is the first publicly recorded incident of a legitimate cyber attack where AI has apparently operated 80-90% of the work. This campaign represents promising advancements in AI integration into traditional attack lifecycles. Allowing threat actors to manipulate AI agents assisting throughout all phases of the attack killchain.

How did the cyber attack work?

The report mentions that the attackers used “MCP servers” paired with Claude Code to execute all the phases of the attack. But what does that actually mean?

To put it simply, MCP (Model Context Protocol) is like a universal USB port for AI models. Usually, an AI like Claude is trapped in a chat box: it can write code for you, but it can’t actually run it within your network. MCP changes that. It acts as a bridge that connects the AI to external tools. LLMs already function as plugins for IDEs like VS Code, but MCP takes this integration a step further. It allows the AI to access and operate external applications, enabling it to run a system like Kali Linux and execute applications and security tools, including Nmap, Metasploit, SqlMap, etc.

Image: Illustration of AI working with MCP servers

In this attack, the threat actors didn’t just chat with Claude. They set up an MCP server that had access to hacking tools (like scanners or exploit kits). Usually all AI agents have usage limitations and rules in place to avoid unethical practices and are extensively trained to avoid harmful behaviour. However, the attackers bypassed this easily by tricking Claude into role-play. Anthropic said, “the human operators claimed that they were employees of legitimate cybersecurity firms and convinced Claude that it was being used for cybersecurity testing.” Instead of refusing the request with a standard safeguard, Claude would receive the command to “scan this target” and send it directly to the MCP server. The server would then execute the tool and return the results to Claude for the next step. Below is an architecture diagram given by Anthropic to suggest how threat actors designed a framework using MCP servers to use Claude Code to conduct the cyber operation without direct human involvement.

Image: Simplified architecture diagram of the operation by Anthropic

What makes this report impressive, is how closely the AI followed a professionally executed Kill Chain. Let’s break down the orchestration. It is a general good practice in engineering to break down complex problems into separate modules. The attackers likely did exactly this. They didn’t ask the AI to “hack this company” in one go. They broke the kill-chain into individual phases such as reconnaissance, initial access, lateral movement, etc.

Image: Attack lifecycle and AI integration architecture given by Anthropic

AI Integration and Attack Lifecycle

The operation mostly used open-source tools and focused on integrating AI via MCP with the traditional eco-system rather than using AI for custom malware development or advanced novel techniques. It’s worth noting that skills for AI deployment as well as Penetration Testing are highly important for these types of eco-systems, because the attackers understood the strength of AI. AIs perform better on open-source tools and widely known methodologies as they might have been trained on them and there’s ample documentation available for them.

Here is how the lifecycle progressed, and I’ve also mapped AI’s involvements aligned to traditionally manual attackers would have executed these steps.

The results of one phase were fed into the next serving two critical purposes:

1. Jailbreaking: By isolating each phase, they could use persona-based role-play (e.g., “You are a penetration tester helping secure this system”) to bypass ethical guardrails. The AI never sees the “whole picture,” so it doesn’t realize it’s committing a crime.

2. Avoiding Degradation: LLMs tend to get confused or perform worse during long, complex tasks. By restarting the context for each phase, the attackers kept the AI sharp and focused.

Is this the future of AI cyber-attacks?

I think we all should ponder on this questions:

What is the truth to be found from this incident? and what lesson can be learned?

There is a lot of skepticism in the community right now, and rightly so. With no details on the actual logs or the technical proof, we only have Anthropic’s summary. It claims the agent ran autonomously for 2-6 hours doing highly technical work, but without verifiable evidence, it’s hard to blindly trust.

In a BBC news article covering this, Martin Zugec from the cyber firm Bitdefender said, “Anthropic’s report makes bold, speculative claims but doesn’t supply verifiable threat intelligence evidence.” There is a real possibility that the cybersecurity industry, much like the AI business, is keen to say hackers are using this tech just to boost interest in their own defensive AI products.

But for us cyber professionals, the question shouldn’t be “if this is true,” but “how much truth is there?”. To do that, we should look at how Claude was used in this cyber-attack, and the current capabilities of AI integration in the cyber security eco-system.

How capable are MCP servers and LLMs for Hacking?

If you look around, you’ll see that the capability is definitely there.

There are already tutorials on YouTube and GitHub showing how to use Claude with MCP servers for penetration testing. NetworkChuck recently released a video demonstrating how to use MCP servers to hack a DVWA (Damn Vulnerable Web App) machine. He showed that the setup is capable of basic tasks like running Nmap scans, using WPScan, and finding vulnerabilities. On GitHub, there are repositories like HexStrike AI, which has over 4.5k stars. These projects are rapidly growing and explicitly focus on AI-powered penetration testing.

Consider MCP server setups as similar to a painters palette: MCP server setup is an AI-operated palette which contains different colours (in this case, security tools) which can be used in many ways. How a penetration tester paints or plays out their methodology is up to them, and having an AI-operated toolkit ready not only makes the setup easy but inherently it’s going to be much faster due to the speed of AI. HexStrike AI is one of many publicly available AI toolkits for penetration testers, and everybody can make one too.

So, there may definitely be some truth to Anthropic’s report. It is possible to integrate LLMs with cybersecurity tools. In the hands of capable people , it might be possible to orchestrate a cyber-attack as big as Anthropic claims. For example an experienced AI developer might focus on integrating AI services with the most appropriate tool based on the task, and create an eco system tailored to the specific objective. On the other hand, an experienced penetration tester might integrate AI with the tools to do analysis of their findings or to create custom malware payloads to exploit a vulnerability based on the target infrastructure. There are multiple ways this can go depending on the user, but one thing that’s common in all cases is that AI integration will definitely increase the effectiveness and efficiency of traditional tasks, one way or another.

Takeaway for Cyber Professionals

Quick Recap: There’s definitely gaps in Anthropic’s report and one might ask various questions. Why did the threat actors use only Claude? Anthropic admitted that Claude “hallucinated” a lot during the attack, claiming to find credentials that didn’t work, so did the attack actually cause significant damage, or was it just a noisy mess?

This incident indicates AI integration in defensive tools could be equally promising, and could be an upcoming shift on AI usage in overall cyber industry. We also hear news about an “AI bubble” that might burst, and some senior researchers suggesting LLMs are hitting a dead end. Many industry leaders on LinkedIn are warning us to be careful of the hype.

However, the growth of MCP servers is very new and shows genuine promise, not just for penetration testing, but for all engineering domains. The only way to know for sure is to actually use this technology yourself, beyond just the prompt-based web UIs.

There is a massive opportunity here for cyber professionals, especially those trying to break into the industry. Don’t just read the headlines. Go build an MCP server. Experiment with the tools. Understand the advancements in AI If there’s true potential in this, you could be riding the wave of the next computer revolution.

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