This Week in AI: ChatGPT Health Risks, Programming for LLMs, and Why Indonesia Blocked Grok

Pour your coffee and settle in. This week brought some of the most concerning AI developments yet, alongside innovations that hint at where this technology is headed. From security breaches to controversial medical features, here's what happened while you were probably asking ChatGPT to debug your code.

The Health Gamble: ChatGPT Gets Medical Access

OpenAI just launched ChatGPT Health, a feature that lets you connect your medical records and wellness apps directly to the chatbot. According to Ars Technica, you can now link Apple Health, MyFitnessPal, and actual medical records so ChatGPT can "summarize care instructions" and "help you prepare for doctor appointments."

Here's the problem: AI chatbots hallucinate. A lot.

Just days before this announcement, SFGate published an investigation about a 19-year-old California man who died from a drug overdose in May 2025 after spending 18 months seeking recreational drug advice from ChatGPT. The chatbot's guardrails failed during extended conversations, and the teen followed the AI's erroneous guidance with fatal consequences.

Now OpenAI wants to process your lab results and medication lists? The same technology that confidently makes up citations and invents legal cases is being positioned as a health advisor. This feels less like innovation and more like playing Russian roulette with people's wellbeing.

The feature claims to be "secure," but security and accuracy are two different beasts. Your data might be encrypted in transit, but that doesn't stop the AI from confidently telling you that your symptoms are nothing to worry about when they're actually signs of something serious.

Security Alert: Your Private Data Is Leaking

Speaking of security - ChatGPT just fell victim to a new attack called ZombieAgent.

According to Ars Technica's security coverage, researchers at Radware discovered a vulnerability that lets attackers surreptitiously exfiltrate your private information directly from ChatGPT's servers. What makes this particularly nasty is that the data gets sent server-side, leaving no traces on your local machine.

Even worse? The exploit can plant entries in ChatGPT's long-term memory for your account, giving it persistence across sessions.

This attack follows a pattern that's becoming depressingly familiar in AI development:

1. Researchers discover a vulnerability
2. Platform adds a specific guardrail
3. Researchers tweak the attack slightly
4. Vulnerability returns

The core issue is that AI chatbots are fundamentally designed to comply with requests. Every guardrail is reactive - built to block a specific attack technique rather than addressing the broader class of vulnerabilities. It's like putting up a highway barrier designed only to stop compact cars while ignoring trucks and SUVs.

Will LLMs ever be able to stamp out the root cause of these attacks? Researchers are increasingly skeptical.

GlyphLang: When Programming Goes Full AI-Native

While we're dealing with security issues, a Hacker News developer dropped something fascinating: GlyphLang, a programming language specifically optimized for AI generation rather than human writing.

The pitch? Traditional languages eat through token limits during long ChatGPT or Claude sessions. The developer kept hitting limits 30-60 minutes into 5-hour coding sessions because accumulated context was burning through tokens.

GlyphLang replaces verbose keywords with symbols:

# Python
@app.route('/users/<id>')
def get_user(id):
    user = db.query("SELECT * FROM users WHERE id = ?", id)
    return jsonify(user)

# GlyphLang
@ GET /users/:id {
  $ user = db.query("SELECT * FROM users WHERE id = ?", id)
  > user
}

The claims are bold: 45% fewer tokens than Python, 63% fewer than Java. That means more logic fits in context, and AI sessions stretch longer before hitting limits.

Before you ask - no, this isn't APL 2.0. APL and Perl are symbol-heavy but optimized for mathematical notation or human terseness. GlyphLang is specifically optimized for how modern LLMs tokenize text. It's designed to be generated by AI and reviewed by humans, not the other way around.

The language already has a bytecode compiler, JIT, LSP, VS Code extension, and support for PostgreSQL, WebSockets, and async/await. Whether this catches on remains to be seen, but it represents an interesting pivot: instead of making AI write human-friendly code, why not create AI-friendly code that humans can still parse?

Grok's Global Mess: Indonesia Says No

Indonesia blocked access to xAI's chatbot Grok this week, and the reason is as disturbing as it is predictable.

According to TechCrunch, Grok was being heavily used to create non-consensual, sexualized deepfakes - particularly targeting women wearing hijabs and saris. Wired's investigation found that a substantial number of AI images generated with Grok specifically target women in religious and cultural clothing.

X's "solution"? They're not fixing the problem - they're just making people pay for it. The platform now requires "verified" (paying) users to generate images, which experts call the "monetization of abuse." And here's the kicker: anyone can still generate these images on Grok's standalone app and website.

This isn't an isolated Grok problem. It's symptomatic of the industry's approach to safety: bolt on restrictions after the fact, then remove them when convenient for growth or revenue.

OpenAI's Questionable Training Data Request

In related "what could go wrong" news, Wired reports that OpenAI is asking contractors to upload real work from past jobs to train AI agents for office work.

The catch? They're leaving it to the contractors themselves to strip out confidential and personally identifiable information.

An intellectual property lawyer quoted in the TechCrunch coverage called this approach one that "puts [OpenAI] at great risk." That's putting it mildly. This is basically crowdsourcing the violation of NDAs and asking workers to self-police what constitutes proprietary information.

Companies spend millions on compliance, legal review, and data protection. OpenAI's approach is essentially "YOLO, just remove the sensitive stuff yourself." What could possibly go wrong?

What This All Means

These stories share a common thread: AI is moving faster than our ability to implement meaningful safety measures.

ChatGPT Health launches despite well-documented hallucination issues. Security vulnerabilities follow a whack-a-mole pattern because the fundamental architecture prioritizes compliance over security. Platforms monetize abuse instead of preventing it. Training data strategies put legal liability on low-paid contractors.

Meanwhile, innovations like GlyphLang show developers are adapting to AI's limitations rather than waiting for AI to adapt to us. That might be the smartest play - work with what AI does well instead of expecting it to master what humans do well.

Here's your morning takeaway with that coffee: Stay skeptical. When a tool tells you something - whether it's medical advice, code, or "facts" - verify it. These systems are powerful, but they're not infallible. Not even close.

And if a company asks you to upload confidential work documents to train their AI? Maybe run that past your legal team first.

What's Your Take?

Are you using any of these AI tools in your workflow? Have you noticed issues with hallucinations or unexpected behavior? Drop your thoughts in the comments - real human thoughts preferred, not AI-generated ones.

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