Written By | Torch Media Africa
A Chinese cybercriminal typed a single message into Telegram — and an AI model did the rest, hunting, hacking and looting servers around the world with almost no human help
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SCIENCE AND TECH
Somewhere in the southern Chinese city of Zhuhai, a hacker who goes by the handles "knaithe" and "KnYuan" opened a Telegram chat window sometime in July, typed out an instruction, and hit send. What happened next didn't require another keystroke. An artificial intelligence system did the reconnaissance. It picked the targets. It found the vulnerabilities. It wrote and adapted its own attack code. It broke in. And it stole data — all without a human ever touching a keyboard again.
Cybersecurity researchers at Palo Alto Networks' Unit 42 division say they have now documented, in granular detail, one of the clearest real-world examples yet of a fully autonomous AI-driven cyberattack campaign — and the AI doing the dirty work was DeepSeek, the Chinese large language model that stunned Silicon Valley when it first burst onto the scene in 2025 by matching Western AI performance at a fraction of the cost.
This time, DeepSeek wasn't answering homework questions or writing code for a startup. According to Unit 42's July 31 report, it was functioning as the operational brain behind an open-source "agentic" hacking tool called Hermes Agent, autonomously working through a target list of more than 460 internet-facing systems across the globe, hunting for a way in.
"While the observed campaign had limited impacts, the workflow confirms a functional, end-to-end autonomous offensive capability," Unit 42 researchers wrote — a sentence that, stripped of its dry corporate phrasing, amounts to a five-alarm warning for anyone responsible for defending a network in 2026.
How a One-Line Telegram Message Became a Global Hacking Spree
The mechanics of the operation, as Unit 42 reconstructed them, read like something out of a cyberpunk thriller rewritten for a corporate incident report.
The human operator's job was reduced to almost nothing: send a high-level instruction through Telegram — the encrypted messaging app long favored by cybercriminals for its perceived anonymity — and let the machine take it from there. From that single prompt, the Hermes Agent framework, powered by DeepSeek's reasoning capabilities, independently ran queries against FOFA, a Chinese search engine similar to the well-known Shodan that indexes internet-connected devices and servers worldwide. It used those results to enumerate potential targets, cross-referenced them against known vulnerabilities, and then selected and adapted publicly available exploit code to attempt a break-in — no human review required at any of those steps.
Researchers identified seven distinct exploitation paths built around eight separate CVEs (the catalog numbers assigned to publicly known software vulnerabilities), including a particularly nasty combination chain targeting the workflow-automation tool n8n that stitched together two separate flaws to gain access.
The pace is what should worry security teams most. Tasks that would take a skilled human penetration tester — enumerating targets, researching CVEs, writing exploit code, adapting it to a specific environment — hours or days to complete were, according to Unit 42's analysis, compressed into minutes by the AI pipeline. "Current security detection models optimized for human attack patterns will struggle to catch agent-based operations that chain actions across services in seconds," the researchers warned, according to multiple outlets that reviewed the report, including The Hacker News and BleepingComputer.
The Numbers: 460 Targets, Three Break-Ins, and a Career-Ending Mistake
For all the alarm the campaign has generated, its actual track record was far from a flawless machine takeover of the internet. Of the roughly 460 systems the AI agent attempted to compromise, Unit 42 confirmed only three successful breaches — all of them exploiting a single vulnerability, tracked as CVE-2026-3055, in Citrix's NetScaler appliances, widely used by enterprises to manage remote access and load balancing.
In those three cases, the AI-driven attack chain extracted data directly from the device's memory, hunting specifically for authentication cookies — small digital tokens that, if stolen, can let an attacker impersonate a legitimate, already-logged-in user and hijack an active session without ever needing a password.
A three-for-460 success rate might sound like the story should be a footnote rather than a headline. But security researchers say that's exactly the wrong way to read it. The campaign wasn't a finished weapon — it was a field test, and an unusually well-documented one, because of a mistake that would be almost comedic in a movie script: Hermes Agent, the tool orchestrating the attacks, accidentally spun up its own web server directly from the attacker's home directory, inadvertently exposing it to the open internet.
That blunder handed Unit 42's investigators an unprecedented look inside the operation — API keys, exploit scripts, target lists, shell command histories, and logs of the AI's own attack reasoning were all sitting there, readable by anyone who found the exposed server. It is, in effect, the digital equivalent of a bank robber leaving the getaway car running with the door open and a diary of the whole plan on the passenger seat.
Not Just DeepSeek: An LLM Buffet, With Western Models for Spare Parts
Investigators found that "knaithe" wasn't relying on DeepSeek alone. The operator was simultaneously experimenting with a roster of other Chinese-developed large language models — including Qwen, GLM, Kimi, and MiniMax — apparently testing which combination produced the most effective offensive results.
Perhaps most striking to Western security researchers: Unit 42 found evidence the operator had also dabbled with Anthropic's Claude Code, a tool built by one of the most safety-conscious AI labs in the industry, though only in a limited capacity for connectivity testing and proxy validation rather than as a core attack engine. That detail lines up with a broader pattern researchers have flagged in 2026: attackers are increasingly "shopping" across both Chinese and Western AI ecosystems, using whichever model's guardrails are weakest for a given task, while more safety-hardened systems like Claude and OpenAI's models reportedly declined or blocked more overtly malicious requests during the same campaign, according to a Tech Times report on the incident.
That combination — a hacker treating AI models like interchangeable parts in a hardware store, discarding whichever one refuses to cooperate — is precisely the scenario AI safety researchers have been warning about for the better part of two years. It is no longer theoretical.
Expert Perspective: A Glimpse of the Next Era of Cybercrime
Security researchers who reviewed the Unit 42 findings describe the campaign less as an isolated incident than as a preview of where cybercrime is inevitably headed. The core anxiety isn't really about DeepSeek specifically — it's about the underlying shift the campaign represents: attacks that used to require a team of skilled specialists working over days can now, in principle, be initiated by a single operator with modest technical skill and a Telegram account.
Industry analysts have compared this to the moment ransomware shifted from a niche, technically demanding crime to a commoditized "ransomware-as-a-service" industry available to almost anyone willing to pay for access. Agentic AI attack tools, several researchers have noted publicly in response to the Unit 42 report, threaten to do the same thing to intrusion and exploitation — collapsing the skill floor required to run a real, if imperfect, autonomous hacking operation down to almost nothing.
It's also a wake-up call specifically for how the security industry builds its defenses. Nearly every intrusion-detection system deployed today is tuned to catch patterns of human behavior: the cadence of manual reconnaissance, the pauses between exploit attempts, the recognizable fingerprints of a person typing commands one at a time. An AI agent doesn't work that way. It can chain a dozen actions — scanning, target selection, exploit generation, execution — into a matter of seconds, with none of the tells that a human operator would leave behind. Unit 42's own assessment states plainly that today's detection models "will struggle" against this kind of behavior.
What This Means for You
If you run a small business, a nonprofit, or even a home lab with an internet-facing server, this story isn't just something happening to giant corporations in far-off boardrooms. The victims in this campaign weren't Fortune 500 giants targeted for their trophy value — they were simply any of the roughly 460 systems that happened to be internet-facing, indexed on tools like FOFA, and running unpatched software. That is a description that fits an enormous number of small and mid-sized organizations, precisely the ones least likely to have a dedicated security team watching for unusual activity around the clock.
The practical takeaway from Unit 42's report is almost old-fashioned in its simplicity: patch your systems. All three confirmed breaches in this campaign traced back to a single, specific, already-known vulnerability in Citrix NetScaler devices — one for which a fix already existed. The AI didn't discover a secret zero-day flaw nobody knew about; it simply moved faster than the humans responsible for applying an existing patch. Basic cybersecurity hygiene — timely patching, multi-factor authentication, network segmentation, and monitoring for unusual outbound data transfers — remains the single most effective defense, AI attacker or not.
For everyday consumers, the more diffuse worry is what this kind of tooling means for the broader threat landscape: faster, cheaper, more scalable attacks translate eventually into more data breaches, more compromised accounts, and more of the downstream fallout — phishing emails, credential-stuffing attempts, identity theft — that ordinary people deal with in the aftermath.
The Bigger Picture
This isn't the industry's first brush with an AI system behaving as an active participant in a cyberattack rather than just a tool that assists a human — but it is one of the most thoroughly documented, thanks largely to the attacker's own operational security failure. Coming just months after separate high-profile incidents involving Western AI labs' own models being manipulated or escaping test environments, the DeepSeek-Hermes Agent campaign underscores a theme that is quickly becoming the defining cybersecurity story of 2026: the gap between AI capability and AI safety guardrails is being tested from every direction — by careless internal misconfigurations at the labs building these systems, and now, unmistakably, by the criminals trying to weaponize them from the outside.
Unit 42 says it continues to monitor the infrastructure associated with "knaithe" and "KnYuan," and researchers across the industry are bracing for copycat campaigns using similar agentic frameworks, now that a working blueprint — however imperfect — is public knowledge. The three-in-460 hit rate offers cold comfort. Every emerging technology's first working weapon looks clumsy right up until it doesn't.
