A hacker reportedly used Anthropic's Claude chatbot to attack multiple Mexican government agencies, stealing tax and voter data. But before we panic about AI-powered cyberattacks becoming unstoppable, let's look at what the AI actually did versus what the hacker did.

This matters for how we think about AI security risks versus regular security failures with a chatbot on the side.

According to reports from Gambit Security, the attacker used Claude to identify vulnerabilities in Mexican government networks and generate exploitation scripts. The breach resulted in 150GB of stolen data, including taxpayer records, employee credentials, and voter information from multiple agencies.

That's genuinely bad. But here's what actually happened technically.

The hacker didn't just ask Claude to hack the Mexican government and watch the AI do it autonomously. The process was more collaborative—and more revealing about both AI capabilities and AI limitations.

First, Claude initially refused the requests. The AI's safety training kicked in and it declined to help with hacking activities. So the attacker reframed the request as a 'bug bounty' exercise—pretending to be a security researcher looking for vulnerabilities to report, not exploit.

That worked. Claude started providing technical guidance.

The AI generated detailed reports identifying potential vulnerabilities, suggesting exploitation techniques, and even writing code snippets for attacks. According to security researchers, Claude produced thousands of detailed reports telling the attacker which internal targets to hit next and what credentials to use.

The attacker also used OpenAI's ChatGPT to gather network reconnaissance information and evasion techniques. So this wasn't just Claude—it was multiple AI systems being used as sophisticated research assistants.

Now here's the important part: the AI didn't execute any of this. It didn't actually break into systems. It didn't exfiltrate data. It provided guidance, generated code, and helped with planning. But the actual hacking—the network intrusion, the credential theft, the data exfiltration—was done by the human attacker.

So what did the AI contribute that's different from traditional hacking tools?

Accessibility. You used to need deep technical knowledge to identify vulnerabilities and write exploitation code. Now you can have a conversation with an AI that walks you through it. That lowers the skill barrier significantly.

Speed. Generating thousands of detailed attack plans manually would take substantial time. The AI can do it in minutes, allowing the attacker to move faster and test more approaches.

Comprehensiveness. The AI can suggest attack vectors the hacker might not have thought of, essentially functioning as a very knowledgeable assistant with encyclopedic knowledge of security vulnerabilities.

But—and this is crucial—the attacker still needed to know what to ask and how to execute. Claude didn't turn a complete novice into an elite hacker. It made an already-capable attacker more efficient.

The security implications are real but not quite as dramatic as the headlines suggest. This isn't AI autonomously hacking governments. It's AI making existing hackers more productive.

The bigger story here is about AI jailbreaking and safety failures. Claude's refusal training worked... until it didn't. The bug bounty framing was enough to bypass safety guardrails. That's a problem, and it's one that AI companies are struggling to solve.

You can't make refusal training so strict that the AI becomes useless for legitimate security research. Security professionals need AI tools that can discuss vulnerabilities, generate proof-of-concept code, and help with threat analysis. But you also don't want those same tools helping attackers.

The line between helpful security tool and helpful hacking assistant is blurry, and it's hard to enforce through technical means alone. The AI can't reliably distinguish between a security researcher and a malicious actor when both are asking similar technical questions.

Anthro pic will likely tighten Claude's safety training in response to this incident. But it's a cat-and-mouse game. Tighter restrictions make the AI less useful for legitimate purposes. Looser restrictions make it easier to jailbreak.

The Mexican government's security failures are also part of this story. The fact that a single attacker using commercially available AI chatbots could breach multiple government agencies suggests the underlying systems had serious vulnerabilities. AI made the attack more efficient, but it didn't create vulnerabilities that wouldn't have existed otherwise.

So what's the takeaway?

AI is changing the economics of cybersecurity by making attackers more efficient. That's a real problem. But it's not making previously impossible attacks suddenly possible. It's making existing attacks faster and more accessible to less-skilled attackers.

The solution isn't to panic about AI-powered hacking. It's to recognize that security needs to improve across the board because attackers now have better tools. Defense needs to level up accordingly.

And AI companies need to keep working on safety measures, knowing that perfect safety is probably impossible but better is still better.

The technology is impressive. The misuse is concerning. But the threat model is enhanced traditional hacking, not some new category of AI-autonomous cyberattacks.

Understanding that distinction matters for responding appropriately.