This was a near miss.
When Anthropic disclosed that state-sponsored hackers exploited Claude to run cyberattacks against 30 organizations, it became the first high-profile Claude AI hack – and a warning shot for AI security.
The headlines screamed about AI autonomy. The model supposedly did 80–90% of the work with only 4–6 human decision points.
But that framing misses what actually happened.
If you’re not deep in cybersecurity, here’s the simple version: the tools your kids, your bank, and your workplace rely on can be manipulated in ways the safety systems were never designed to catch.
How Attackers Really Used Claude in the Hack
Social Engineering for Machines
The attackers didn’t overpower Claude AI with sophisticated exploits. They tricked it.
They fragmented malicious operations into tiny, seemingly innocent requests. Think of it as smuggling disassembled gun parts through airport security. Each piece looks harmless. Once through, they reassemble into something dangerous.
The technique works because of a fundamental weakness: context.
AI models don’t read text the way you and I do. They process it as separate chunks called tokens. In simple terms, they’re looking at “exp”, “losiv”, and “es” one piece at a time, not “explosives” as a whole word.
Each token passes the safety checks individually. The model doesn’t connect them into “explosives” until it’s too late. The guardrails are looking for complete weapons, not individual components.
This is social engineering adapted for AI. Instead of manipulating human trust, attackers manipulate how models parse language.
Research shows these multi-turn fragmentation attacks achieve a 65% success rate compared to just 5.8% for direct malicious requests. In other words: if you try to get an AI model to do something obviously malicious in one go, it usually says no. Break that same request into lots of tiny, harmless-looking steps, and your odds of success jump more than tenfold.
That’s why the Claude AI hack matters: it showed how easy it is to hide malicious intent inside lots of tiny, harmless-looking requests.
Fragmented AI Attacks Expose a Context Problem
This isn’t just a Claude problem. It’s a model problem.
Every major AI system today has the same basic limitation: they’re brilliant at pattern-matching inside a single prompt, and terrible at spotting slow-burn patterns spread across dozens or hundreds of prompts.
Attackers are already exploiting that gap. Instead of sending one big “help me run a cyberattack” request, they send a drip-feed of small, plausible questions:
- “Can you help me write a script that scans open ports?”
- “Now show me how to log the results to a file.”
- “Now help me filter that file for specific IP ranges.”
Each step looks fine in isolation. Put them together and you’ve just used an AI assistant to assemble a cyberattack.
The Claude AI hack was the first big public example of this, but it won’t be the last.
The Privacy Problem Nobody Wants to Solve
Here’s the dilemma that keeps me up at night.
To catch these attacks, AI systems need to connect the dots across multiple requests. They have to notice that all those “innocent” prompts add up to something dangerous.
But if those requests come from different accounts, or different apps using the same underlying model, you’re suddenly surveilling user activity to detect patterns.
Privacy versus security. Pick one.
The theoretical solution is neat on paper: detect attack patterns from anonymised requests, escalate only when you’re confident something’s wrong, and keep individual users unidentifiable for as long as possible.
But that’s hand-waving. The practical reality is messier, more invasive, and nobody has figured it out yet.
Any serious AI security system has to decide how much user activity it’s willing to monitor in order to catch these patterns. That’s not just a technical question – it’s a political and ethical one.
Why the ‘AI Autonomy’ Narrative Is Misleading
Let’s talk about those “4–6 decision points” that supposedly prove AI autonomy.
That number hides the real story. The bulk of the work went into crafting prompts and setting up agents beforehand. Skilled attackers spent significant time engineering the fragmentation technique, testing it, refining it.
The “80–90% AI autonomy” claim in Anthropic’s report makes Claude sound like it independently ran the operation. But that framing hides the real work: humans designing and testing the fragmentation strategy until the model did what they wanted.
The “AI autonomy” narrative serves two purposes:
- It positions Anthropic as the hero who can fix the problem.
- It makes their model sound impressively powerful.
Smart marketing. Questionable framing of the actual threat.
If you’re a parent trying to work out how worried you should be about AI systems your kids are using, here’s the key point: the danger isn’t a rogue, self-directed AI. It’s smart humans learning how to bend these systems around the safety checks.
We’re Behind on AI Security Talent and Tooling
Network security engineers monitor thousands of requests per second because they have mature tools and decades of experience. They know what normal looks like. They can spot anomalies.
AI security? We’re starting from scratch.
We need specialists who understand model behavior at a deep level. People who think like prompt engineers and hackers simultaneously. They’d work with AI to identify patterns of individually benign requests that combine into breaches.
We don’t yet have AI security engineers in the same way we have network security engineers – people whose full-time job is understanding how models can be abused and how to stop it.
The tooling doesn’t exist either. We’re building increasingly capable AI systems faster than we’re developing the security infrastructure to protect them.
Companies prioritise shiny new models over unglamorous security work. Great for PR. Terrible for long-term trust.
Waiting for the First Truly Catastrophic AI Breach
I wish I could end this with optimism about industry self-regulation or innovative security solutions.
But here’s what I actually believe: nothing significant happens until an attack gets through.
This Claude AI hack targeted 30 organizations across finance, tech, chemical manufacturing, and government. It was sophisticated. It was largely automated. It demonstrated a new attack vector.
And it was a near miss.
Regulation will eventually force companies to invest in AI security. But regulation follows disaster, not warnings. We’re in the waiting period before the catastrophic breach that changes everything.
The question isn’t whether that breach will happen. It’s what we lose when it does.
Until then, treat “AI safety” claims the way you’d treat a new defender in your team’s back line: don’t trust the hype until you’ve seen them tested under real pressure.
If you want the non-hyped version of what’s actually happening with AI – the stuff that sits between PR and panic, and what it means for your family’s everyday tech – that’s exactly what I break down in my newsletter. Join here and stay one step ahead while everyone else is still reading the headlines.
FAQ: The Claude AI Hack and AI Security
Q1: What actually happened in the Claude AI hack?
In the Claude AI hack, state-sponsored attackers used Anthropic’s Claude model to help run cyberattacks against around 30 organizations. They didn’t “take over” the model. Instead, they broke a malicious operation into lots of tiny, harmless-looking prompts. Claude helped with each small step, and those steps added up to a coordinated cyber-espionage campaign. It was a near miss, but it exposed a serious blind spot in how current AI guardrails work.
Q2: Why is the Claude AI hack such a big deal for AI security?
The Claude AI hack matters because it showed that you don’t need to jailbreak a model in one big, obvious prompt. You can hide malicious intent inside many small, fragmented requests that look safe on their own. Today’s AI safety systems are good at spotting single, clearly bad prompts. They’re much worse at spotting slow-burn patterns across dozens or hundreds of interactions. That’s a fundamental AI security problem, not just a Claude problem.
Q3: Does this mean AI models are becoming fully autonomous and dangerous?
No. The Claude incident doesn’t prove that AI is “going rogue.” It proves that skilled humans can use AI as a powerful tool inside an attack. The so-called “80–90% autonomy” figure mostly reflects how much of the grunt work the model handled once the attackers had carefully engineered their prompts and strategy. The real risk isn’t a self-directed AI mastermind. It’s humans learning how to bend these systems around their safety checks.
Q4: What does the Claude AI hack mean for everyday users and parents?
For most people, the takeaway is simple: don’t assume “AI safety” labels mean “problem solved.” The same kinds of fragmented attacks used in the Claude AI hack could, in time, be aimed at tools your kids use, your bank, or your workplace. You don’t need to panic, but you should stay sceptical of big marketing claims and pay attention to how seriously companies talk about AI security, not just new features.
Q5: What needs to change to stop attacks like the Claude AI hack?
We need two big shifts. First, better tooling and roles: dedicated AI security engineers who understand how models behave and how attackers think, plus systems that can spot patterns across many small prompts, not just single bad requests. Second, honest trade-offs around privacy and monitoring: you can’t detect fragmented attacks without looking at user behaviour in some way. Until regulation and industry standards catch up, we’re relying on companies to do the right (and often expensive) thing on their own.