So, are we all meant to be cowering in fear of the new AI-powered super-hacker? The narrative we’re being sold is one of autonomous, silicon-based geniuses crafting undetectable malware and running circles around our best cyber defences. It’s a great plot for a film, but the reality, as is often the case, is far more mundane and, frankly, a bit more comical. The latest dispatches from the front lines of geopolitical cybersecurity don’t show us a flawless cyber-ninja; they show us a clumsy apprentice fumbling with a new tool.
The conversation around AI-optimized cyberattacks has been dominated by hypotheticals. But now, thanks to some excellent research from Proofpoint, we have a real-world case study. A Chinese state-aligned group decided to take Large Language Models (LLMs) for a spin against Taiwanese infrastructure. The results weren’t exactly the dawn of Skynet. Instead, it was a timely reminder that a powerful tool in the wrong hands doesn’t make the user powerful; it just exposes their incompetence more efficiently.
What are ‘AI-Optimized Cyberattacks’ Anyway?
Before we dive into the farce, let’s get our terms straight. What do we actually mean when we talk about AI-optimized cyberattacks? Forget the image of a sentient AI deciding to steal your credit card details. Think of it more as a set of productivity tools for the discerning cybercriminal. At its current stage, AI is being used as an assistant to augment existing threat actor tactics, not to invent entirely new ones from scratch.
Hackers are experimenting with LLMs for a few key tasks:
– Crafting Phishing Emails: The most obvious use case. Instead of relying on tired, typo-ridden templates, attackers can ask an AI like ChatGPT to generate convincing, grammatically correct, and contextually relevant emails to trick employees. The goal is to bypass the human “spam filter” that catches obviously fake messages.
– Generating Malicious Code: An attacker can use an AI to write scripts for specific tasks, like data exfiltration or creating a backdoor. It can also be used to obfuscate code, making it harder for antivirus software to detect.
– Reconnaissance and Social Engineering: AI can quickly sift through public data—social media profiles, company websites, news articles—to build detailed profiles of targets, making social engineering attempts far more personalised and plausible.
Imagine a burglar who suddenly gets access to Google Maps Street View, architectural blueprints from the council website, and a crowbar-wielding robot. The burglar still needs to know where to look, what to steal, and how to get away with it. The tools make parts of the job easier and faster, but they don’t provide the fundamental criminal strategy. The same is true for AI in hacking. It’s a force multiplier, for better or worse.
Case Study: DropPitch and the Testing of AI Tools
Enter our protagonists, a Chinese-aligned threat group that Proofpoint tracks as TA427, also known as DropPitch. As detailed in a recent Dark Reading report, this group has a history of targeting organisations that displease Beijing, with a particular focus on Taiwan. Between June and July of this year, they turned their attention to over half a dozen Taiwanese organisations, primarily in the critical semiconductor and financial sectors. This time, they brought some new toys: LLMs like OpenAI’s ChatGPT and a China-based model called DeepSeek.
Their plan seemed to be textbook AI augmentation. First, they attempted to use the models to generate phishing emails designed to look like legitimate business communications. The idea was to sound more professional and bypass suspicion. Second, they experimented with using AI to help with their backdoor malware, likely to refine commands or obfuscate the code to avoid detection by security software.
It was a classic test of whether these new tools could give them an edge. The answer, it turns out, was a resounding ‘no’.
Lessons Learned from DropPitch’s Botched Job
The AI-generated content was, to put it mildly, a mess. Instead of creating slick, professional emails, the output was awkward, overly formal, and in some cases, downright bizarre. The tone was all over the place. One of the more memorable failures highlighted by the Proofpoint researchers was a German-language phishing lure that incongruously included a link to Pornhub. You can’t make this stuff up. It’s the digital equivalent of wearing a tuxedo with clown shoes.
The attack screamed ‘unprofessional’. It lacked the subtlety and social awareness that a skilled human attacker brings to the table. The LLM, for all its linguistic power, couldn’t grasp the nuanced context of a corporate environment. It was simply stringing words together based on its training data, resulting in content that felt uncanny and ‘off’. As a result, DropPitch’s grand experiment was a failure. They quickly abandoned their AI-generated lures and reverted to their older, human-crafted (and presumably more effective) methods.
This episode perfectly illustrates a crucial point, best summarised by Alexis Dorais-Joncas of Proofpoint: “AI used by incompetent people will lead to incompetent results.” This is the core takeaway. An AI is not a magic wand that bestows skill. If a threat actor is unsophisticated, using an AI will just produce more sophisticated-sounding nonsense. It can amplify their output, but it also amplifies their underlying lack of judgment and expertise.
The Current Landscape of Geopolitical Cybersecurity
This incident isn’t just a funny story about bad hackers; it’s a symptom of the broader landscape of geopolitical cybersecurity. State-aligned groups like DropPitch aren’t always the elite, Mission: Impossible-style teams we imagine. Nations employ a wide spectrum of cyber operatives, from highly skilled intelligence units to lower-level digital mercenaries and patriotic “script kiddies.” DropPitch appears to fall somewhere in the middle—persistent and state-directed, but lacking the finesse of the top-tier players.
Their choice of targets—Taiwanese semiconductor firms—is no accident. This is where cyber meets geopolitics. Taiwan’s dominance in the global chip market is a major point of economic and strategic tension. Disrupting or spying on these companies serves clear strategic goals for Beijing. This context is critical; these aren’t random attacks. They are pointed, political acts carried out in the digital realm.
However, this also highlights the very real defense limitations that organisations face. Even a clumsy, AI-generated attack campaign creates noise. Security teams have to investigate these alerts, wasting time and resources that could be spent on countering more advanced threats. As more low-skilled groups experiment with AI, we can expect a tsunami of low-quality but high-volume attacks. The challenge for defenders will be to automate the filtering of this AI-generated chaff so they can focus on the genuinely dangerous wheat. How do you prepare for a thousand clumsy robots knocking at the door at once?
Threat Actor Tactics and Future Trends
So, if DropPitch’s attempt was a flop, does that mean we can all relax about AI-optimized cyberattacks? Absolutely not. This was merely the opening act. This was the equivalent of the Wright brothers’ first flight at Kitty Hawk—it didn’t go very far, and it looked a bit rickety, but it proved flight was possible.
The threat actor tactics will evolve. Right now, attackers are using AI for:
– Social Engineering at Scale: Generating thousands of unique, personalised phishing emails.
– Code Generation: Asking AI to write simple malicious scripts or functions.
– Vulnerability Discovery: Using AI to scan open-source code for potential weaknesses.
But what happens next? The real danger isn’t incompetent actors using AI poorly. The real danger is competent actors using AI brilliantly. Imagine a sophisticated group pairing its deep expertise with an AI assistant. They could automate the most time-consuming parts of an attack, such as reconnaissance and lateral movement, freeing up human operators to focus on the creative, strategic elements of a breach.
We are likely heading towards a future where AI is used to:
1. Create Polymorphic Malware: Viruses that use AI to rewrite their own code every time they infect a new machine, making them almost impossible to detect with traditional signature-based antivirus.
2. Automate Hacking Campaigns: An AI could be directed to “breach company X and find their R&D data,” and it could then autonomously probe defences, identify weaknesses, and execute the attack chain with minimal human oversight.
3. Generate Hyper-Realistic Deepfakes: Imagine a CEO making a video call to the finance department, authorising a multi-million-pound transfer. Except it’s not the CEO; it’s an AI-generated deepfake. The technology is already getting scarily good.
The DropPitch incident represents the clumsy, experimental phase. The next phase will see AI seamlessly integrated into the workflows of skilled attackers, making them faster, stealthier, and more effective.
Preparing for the Inevitable
The story of DropPitch is a valuable, if amusing, lesson. It shows us that for now, the human element—skill, strategy, and judgment—remains the most critical factor in a successful cyberattack. The panic over AI-optimized cyberattacks replacing human hackers is premature. The immediate threat is one of volume and noise, as more low-skilled groups follow DropPitch’s lead.
But we can’t afford to be complacent. Defenders need to start thinking about adaptive strategies. This means not only updating technical defences but also doubling down on human training. Employees need to be taught to spot the subtle, uncanny valley weirdness of AI-generated content, not just obvious spelling mistakes.
For the cybersecurity industry, the race is on to build AI-driven defences that can detect and neutralise AI-driven attacks. The future of cyber defence won’t be about building higher walls; it will be about creating intelligent, autonomous systems that can identify and respond to threats in real-time.
The clumsy robot is learning. It will fall over a few more times, but eventually, it will learn to walk, and then to run. The question for us is, will we be ready when it does? What do you think is the biggest blind spot for organisations when it comes to defending against this next wave of AI-assisted threats?
