The honest one-liner

OpenClaw isn’t malware. It’s a legitimate open-source project run by a non-profit foundation. The danger isn’t the app itself. It’s what the app is allowed to do, and who else has learned to abuse that.

Remember the eager intern with the keys to your laptop? Same intern. This is the chapter where we talk about what happens when someone slips the intern a fake instruction. (New here? Start with What Is OpenClaw?)

Scare #1: the one-click hack

In early 2026, security researchers found a serious flaw in OpenClaw — officially CVE-2026-25253, rated 8.8 out of 10 on the industry severity scale (that’s “drop everything and patch” territory).

In plain English: if your OpenClaw was reachable from the internet, a single malicious link could let a stranger run commands on your machine. Not “see your data” — run commands. On a tool that’s already wired into your email, files, and messages.

It’s been patched. But it tells you the shape of the risk: when powerful software has a bug, the bug inherits all that power.

Scare #2: the poisoned add-on store

This is the big one, and it’s worse than a single flaw.

OpenClaw lets you install community-made “Skills” — little add-ons that teach it new tricks — from a store called ClawHub. Sounds great. Except security firm Koi Security audited all 2,857 Skills in the store and found 341 of them were malicious, roughly 1 in 8.

What did the bad ones do? They quietly installed an info-stealer (a nasty piece of malware-for-hire called AMOS) that hunts for exactly the things you don’t want stolen: browser passwords, saved logins, SSH keys, and crypto wallets. More than 100 of the fake Skills disguised themselves as crypto tools — think fake Solana or Phantom wallet helpers — aimed straight at people holding cryptocurrency.

The really uncomfortable detail: the attackers specifically targeted people running OpenClaw 24/7 on an always-on machine, which is exactly what enthusiasts are told to do. The setup that makes it most useful is also the one that makes it the juiciest target.

Why it’s riskier than a normal app

A normal app runs in its own little box and asks before it touches your stuff. OpenClaw is the opposite by design: you give it broad access so it can actually be useful. That’s the whole point, and the whole problem. When you hand something that much power, a single bad add-on or bad link doesn’t cause a small mess. It causes a big one.

There’s also no safety net you’d expect from a big company: as of early 2026, OpenClaw had no bug-bounty program and no dedicated security team. It’s volunteers moving fast. Big enterprise security firms have flagged it as a serious insider-threat risk for exactly this reason.

So can you actually use it safely? Yes — with rules.

If you still want to play with it (and honestly, it’s a fascinating toy), don’t install it on your everyday laptop. Do this instead:

• Update it the moment an update appears. This is rule number one for a reason. The 8.8 hack from Scare #1 was killed by a patch, and the people who got burned were running old versions. Boring, unglamorous, and the single most effective thing you can do.
• Run it on a separate machine: a cheap cloud server (VPS), a virtual machine, or an old spare laptop that holds nothing sensitive. Never your main device. (Heads up: a VPS is on the internet by default, so the “keep it off the open internet” rule below isn’t optional there.)
• Never run it as the “root” / admin user. If it gets tricked, you want it to have as little power as possible.
• Keep it off the open internet. There’s a setting that binds it to your own machine only (127.0.0.1 instead of 0.0.0.0). Turn that on. It won’t fix a bad update, but it keeps random internet strangers from finding your instance in the first place.
• Treat every community Skill like a stranger’s USB stick. Check who made it, look at the code if you can, and never install one that asks for more access than its job needs.
• Keep zero crypto keys and passwords on that machine. If there’s nothing to steal, a stealer goes home empty-handed.

Who should just walk away

If you’re not comfortable spinning up a spare machine and toggling security settings, that’s not a failure — it’s good judgment. OpenClaw is currently a power-user’s toy with power-user’s risks. For “I just want AI to help with everyday stuff,” a normal chatbot in your browser gives you 90% of the benefit and roughly none of this headache.

The bottom line

OpenClaw is safe if you cage it: updated, on a separate machine, low privileges, off the open web, no valuables inside, and a deep suspicion of community add-ons. Run it that way and it’s a genuinely cool experiment. Run it on your main laptop with the store wide open, and you’re the target the whole story was warning you about.

Wondering how it stacks up against a tool that does come with guardrails? Read OpenClaw vs Claude Code →.

The post Is OpenClaw Safe? The Honest Truth About the Risks appeared first on DumbItDownAI.

In early 2025, a Chinese app called DeepSeek showed up, matched ChatGPT, and cost a fraction of the price. Governments panicked: within weeks it was banned on government devices in the US, Australia and Taiwan, and yanked from app stores in Italy and (briefly) South Korea.

So the obvious question: are Chinese AI models dangerous — or is that panic overblown? Here is the honest answer, no hype. First an actual comparison of what these models can do, then the part most articles skip: how to use them safely.

The 30-Second Version

• The models are genuinely good. DeepSeek, Alibaba’s Qwen, and Kimi match ChatGPT and Claude on many tasks (coding, math, long documents) — often for free.
• The privacy problem is real, but misunderstood. The issue is not where the model was made — it is where your data gets processed. Type into the official DeepSeek app and your data goes to servers in China, where local law can compel access.
• You can use them safely if you know how: keep sensitive data out of the cloud app, or run the open model yourself / through a Western provider.

Why Everyone Suddenly Cares About Chinese AI

For years, “cutting-edge AI” meant American AI. That changed in early 2025 when DeepSeek arrived — a model that could go toe-to-toe with ChatGPT at a fraction of the cost. Since then the field has exploded: Alibaba’s Qwen, the free and openly-available Kimi from Moonshot AI, plus GLM and MiniMax.

For you, that means more choice — often free. But it also raises a question worth taking seriously: what happens to your data? Let’s do the comparison first, then the safety part properly.

The Three Chinese Models That Matter

DeepSeek — the price-performance shock

DeepSeek (founded 2023, currently the V3/V4 generation) is the model that started it all. Strong at reasoning and coding, and extremely cheap per token. Its models have open weights — meaning you can download and run them yourself (though the training code stays private).

• Great at: reasoning, math, code, cost
• Weak spot: the official app is the one governments keep flagging (more below)

Qwen (Alibaba) — the versatile all-rounder

Alibaba’s Qwen (now the Qwen3.5–3.8 generation) is the all-rounder. It is multimodal (text, image, code) and handles a huge range of everyday tasks. Many Qwen models have open weights — though the newest flagships do not. A serious free alternative for most tasks.

• Great at: versatility, multimodal tasks
• Weak spot: no single standout identity like DeepSeek (price) or Kimi (technical)

Kimi (Moonshot AI) — the open technical star

Kimi (K2/K3) is the favorite for math, logic and code — and for very long documents, thanks to a massive context window. Completely free to use, with open weights. One catch: on benchmarks it hallucinates a bit more than the top US models on pure factual recall, so double-check facts. We broke it down in our big model comparison.

• Great at: math, code, long texts, cost (free)
• Weak spot: factual recall from memory

Also in the race: GLM (from Zhipu / Z.ai) and MiniMax — both technically strong and fully English-capable, but they show up more as developer and coding tools than as a friendly chat app you’d stumble across.

The Real Question: How Safe Is Your Data?

This is where the most nonsense gets spread. Let’s start with what actually happened:

Sounds like a clear verdict: stay away from Chinese AI. But it is not that simple — and that mix-up costs you good, free tools.

The key distinction: origin ≠ risk

Legally and technically, what matters is not where the model came from, but where your data gets processed. Run an open Chinese model on your own computer or through a Western/EU provider, and the China-specific data-transfer problem simply disappears — your data never goes there. (Normal privacy rules still apply, of course.)

The risk is not in the model — it is in the route you use it through. That is exactly why the app gets flagged, not the model itself.

How to use Chinese models safely — the traffic-light rule

Think in three levels, from risky to safe:

And this applies to every AI — not just China

Important context: ChatGPT, Claude and Gemini also process your input on someone else’s servers (usually in the US). So the basic data-hygiene rules apply everywhere:

• Never paste sensitive data: no passwords, API keys, client data, health or financial info. More on that in our AI privacy guide.
• Anonymize: swap names, addresses and numbers for placeholders before you paste.
• Turn off training: most services let you stop your inputs being used to train the model.
• Separate work and personal: use vetted, compliant accounts for client data — not your free personal login.

The Bottom Line: Who Should Use Chinese Models?

Chinese AI models are no longer toys — they are a serious, often free alternative. Whether they are right for you depends less on where they were made than on how you use them:

• For harmless personal tasks (ideas, translations, learning, coding with no sensitive data): go for it — use Qwen or Kimi for free and skip a subscription.
• For business work with client data: only through an EU/Western provider with a proper agreement, or self-hosted. Otherwise stick to vetted tools.
• For maximum data control: run an open model locally — the only way your data is guaranteed to stay with you.

Or as we say around here: the country a model comes from does not decide whether it is safe — how you use it does. Keep the traffic light in mind, and you can happily experiment with even the most exciting Chinese models.

Go deeper: Compare the big players in ChatGPT vs Claude vs Gemini (now with Kimi), find out which AI hallucinates the least, and lock things down with AI Safety 101: Protect Your Privacy.

The post Chinese AI Models: Are DeepSeek, Qwen & Kimi Safe to Use? (2026) appeared first on DumbItDownAI.

What is a residential proxy, in plain English

A residential proxy is a network that routes someone else’s internet traffic through a real home’s IP address — yours, potentially. To whatever website or server receives that traffic, it doesn’t look like a bot or a data center. It looks like a normal household doing normal household things. Checking email. Watching a show. Being boring and legitimate.

That’s precisely why criminals love it. A datacenter IP or a commercial VPN gets flagged fast — security systems treat those as infrastructure and block them on sight. A real residential IP, though, sails through almost every filter, because it’s supposed to belong to an actual person. Congratulations, you might be that person.

How your device gets recruited

Nobody signs a contract that says “please turn my TV into criminal infrastructure.” It happens quieter than that, usually one of three ways.

1. It’s baked into a “free” app

Developers embed a proxy SDK inside free apps — games, utilities, smart-TV apps, the kind you install because paying $3 for an app felt unnecessary. In exchange for “free,” you agree to share some bandwidth and your IP address. It’s usually in the terms and conditions, in the section absolutely nobody reads, which is exactly the point.

2. It ships pre-installed

Some cheap Android TV boxes and streaming sticks arrive with the malware already on board, straight from the factory. This is the mechanism behind the BADBOX and “SuperBox” cases — devices that were compromised before they ever left the warehouse. You didn’t download anything shady. The shady part was already inside the box.

3. You technically opted in

Then there are the apps that promise to pay you for your idle bandwidth, Honeygain-style. Some people genuinely know what they’re signing up for. Most just see “earn a little money doing nothing” and don’t ask the obvious follow-up question: doing nothing, for whom, exactly?

How big this actually is

These aren’t fringe cases. In 2026, the scale became impossible to ignore — and it’s worth keeping the individual cases separate, because each one is large enough to stand on its own.

• NetNut network: Google and the FBI took down a proxy network built on roughly 2 million hijacked devices, mostly smart TVs and streaming boxes.
• IPIDEA: in a separate action reported by Google in January 2026, investigators found around 6.1 million IPs active on any given day, drawn from an estimated 5 million infected devices. Recruitment ran through more than 600 Android apps and 3,075 Windows binaries. Over 550 criminal groups reportedly used the network — for everything from espionage to credential-stuffing attacks to DDoS campaigns.

John Hultquist of Google Threat Intelligence summed it up bluntly: residential proxy networks have “become a pervasive tool for everything from high-end espionage to massive criminal schemes.” Not a niche problem. Infrastructure.

Why this is your problem, specifically

Here’s the part that turns an abstract security story into a personal one. If your device is part of one of these networks, someone else’s traffic — quite possibly criminal traffic — exits to the internet through your connection. Your IP address becomes, from the outside, the source of whatever they’re doing: scanning, fraud, credential attacks. If anyone traces it back, the trail leads to your home, not theirs.

Less dramatic but still annoying: your internet gets slower, and your data usage creeps up, because your bandwidth is now doing a second job you never agreed to.

How to check if you’re affected

This is the part where being mildly paranoid actually pays off.

• Run the GreyNoise IP Check. It’s a free tool that tells you whether your public IP has been observed doing suspicious scanning or known botnet activity — a quick way to see if your address has a reputation you didn’t authorize.
• Watch for the quiet warning signs: a network that’s mysteriously sluggish, high data usage when every device should be idle, or unfamiliar apps sitting on VPN or proxy permissions they never needed in the first place.

How to protect yourself

The FBI’s 2026 guidance on this is refreshingly unglamorous — no exotic security software required, mostly just fewer bad habits.

• Skip the no-name streaming boxes promising “free” live sports or movies. If the business model doesn’t make sense, you’re probably the product — or worse, the infrastructure.
• Avoid sketchy free VPN apps, especially ones with vague ownership and glowing five-star reviews that read suspiciously alike.
• Stay away from pirated or “cracked” software. It’s one of the most common places proxy malware hides.
• Stick to official app stores, check what permissions an app actually asks for, and leave Google Play Protect switched on if you’re on Android.
• Keep your smart TV and streaming box firmware updated, and disconnect devices you’re not actually using from the network.

If you actually want a VPN, don’t grab a free one

Here’s the irony worth sitting with: a lot of the “free” VPN apps in the app stores are the exact thing this article is warning you about. Running a VPN costs real money — servers, bandwidth, maintenance — so some free ones quietly pay for themselves by reselling your bandwidth and IP address. In other words, the “privacy tool” turns you into one of those residential proxy nodes.

If you genuinely want a VPN — for public WiFi, general privacy, or just peace of mind — the rule is simple: pay for a reputable one that makes its money from subscriptions, not from renting out your connection.

Two solid, no-nonsense options:

• NordVPN — fast, huge server network, strong encryption. From about $3.50/month.
• Surfshark — cheapest option, unlimited devices. From about $2.50/month.

Bottom line

Residential proxy botnets are invisible by design, already enormous, and still growing. There’s no dramatic warning screen when your TV gets recruited — it just quietly starts working a second job. The good news is that the fix isn’t complicated: buy from real brands, be suspicious of anything free that shouldn’t be, skip the cracked software, and run the IP check every once in a while. Treat “too good to be true” as the security warning it usually is, and you’ve already handled most of the risk.

Keep reading: Go deeper on staying safe online with our AI Safety 101 guide, and see how the same tech powers AI-generated fake profiles.

Sources: Google Threat Intelligence (IPIDEA/NetNut takedowns, 2026), FBI (residential proxy alert, 2026), GreyNoise (IP Check).

The post What Is a Residential Proxy? How Your Smart TV Ends Up in a Botnet — and How to Check appeared first on DumbItDownAI.

You have seen it. That LinkedIn post that starts with “I’m thrilled to share” and says absolutely nothing in 300 words. The Google result that reads like a robot summarizing a robot. The Facebook image of Jesus made entirely of shrimp — with seven fingers on one hand.

Welcome to the era of AI slop.

It is the junk food of the internet. Mass-produced, zero-nutrition content pumped out by AI tools with no human oversight, no editing, and no reason to exist other than farming clicks. And it is everywhere.

Here is what AI slop actually is, why it is ruining the internet, how to spot it — and most importantly, how to make sure you are not creating it.

What Is AI Slop, Exactly?

AI slop is low-quality, mass-produced AI-generated content that gets published with little or no human editing, fact-checking, or original thought added.

Think of it like this: AI is a kitchen blender. In the right hands, it makes amazing smoothies. Left unattended, it just sprays stuff everywhere.

The term started gaining traction in early 2024, largely thanks to developer Simon Willison (co-creator of Django), who argued the internet needed a word as dismissive as “spam” for unwanted AI output. By mid-2024, The Guardian, Washington Post, and the New York Times were all using it regularly.

It stuck — because we all recognized the problem the moment we had a word for it.

AI Slop in the Wild: Greatest Hits

If you think this is a minor issue, here is what has actually happened:

The Shrimp Jesus Era

Facebook feeds got flooded with AI-generated images of “Jesus made of shrimp,” “soldiers hugging families,” and “disabled children with birthday cakes.” These were not art projects. They were engagement-bait posted by content farms in Southeast Asia, designed to collect followers, then sell the pages. Millions of people interacted with them.

Sports Illustrated’s Fake Authors

In November 2023, Futurism exposed that Sports Illustrated had been publishing AI-written articles under completely fake author profiles — complete with AI-generated headshot photos. The “authors” had names, bios, and LinkedIn-style pictures. None of them were real people.

The Amazon Kindle Flood

Amazon got so overwhelmed with AI-generated books (some listing “ChatGPT” as co-author) that they had to cap self-publishing to three titles per day in September 2023. The worst part? Some of those books were mushroom foraging guides with dangerously wrong information. Following bad mushroom advice is not a typo you walk off.

CNET’s Silent AI Experiment

CNET — yes, the tech publication that has been around since the dial-up era — secretly used AI to write financial advice articles in early 2023. Multiple articles contained factual errors. They got caught, and it was not pretty.

The LinkedIn Thought Leader Epidemic

You know the posts. “In today’s fast-paced world…” followed by four paragraphs of absolutely nothing, topped with “Let that sink in.” and “Agree? ” LinkedIn became ground zero for AI-generated pseudo-wisdom. If a post could have been written about literally any topic and still make equal sense, it is probably slop.

Why Should You Care?

This is not just an aesthetic problem. AI slop is actively making the internet worse.

• Search results are degrading. Originality.ai estimated that roughly 14% of top Google search results contained AI-generated text as of early 2024. That number is climbing.
• Trust is evaporating. The Reuters Digital News Report 2024 found trust in online news at historic lows across every market surveyed.
• Model collapse is real. When AI trains on AI-generated content (which is increasingly happening), quality degrades recursively. Researchers at Oxford and Cambridge demonstrated this mathematically in 2023. The internet is eating its own tail.
• Europol’s warning: By 2026, up to 90% of online content could be synthetically generated or manipulated. We are basically there.

The “Dead Internet Theory” — the idea that most online interaction is already bot-to-bot — used to sound paranoid. It is starting to sound like a weather forecast.

How to Spot AI Slop

Good news: AI slop has tells. Once you learn them, you cannot unsee it.

In Text

• The hedge parade: “It’s important to note,” “It’s worth mentioning,” “In today’s fast-paced world” — if a sentence could be deleted and nothing is lost, it is filler.
• Saying things twice: The same point, rephrased slightly, in back-to-back paragraphs. AI loves restating the title five different ways.
• Zero specifics: No names, no dates, no sources, no personal experience. Just vibes.
• Suspiciously smooth tone: Real humans are messy writers. If every paragraph flows like a Wikipedia entry, something is off.
• The ghost opinion: “Many experts believe…” Which experts? Where? When? AI often attributes opinions to unnamed masses because it cannot cite actual sources.
• “As of my last update” — if you see this in a published article, someone literally copy-pasted from ChatGPT without reading it.

In Images

• Too many fingers (or too few, or fused ones)
• Text on signs or shirts that looks like a language from another dimension
• Overly smooth, plastic-looking skin
• Earrings, glasses, or jewelry that do not match on both sides
• Backgrounds that make no architectural sense when you actually look

How to NOT Create AI Slop

Here is the thing — AI is an incredible tool. The problem is not AI. The problem is people who treat “generate” as a synonym for “publish.”

If you use AI for writing, creating, or working, here is how to stay on the right side:

1. AI Is Your Draft, Not Your Publisher

Use AI to get started. Use your brain to finish. The best AI-assisted content has a human fingerprint on every paragraph — personal experience, specific examples, opinions that an AI would never generate because it has no life to draw from.

2. Write Better Prompts

Vague prompts create vague output. “Write me an article about productivity” will give you slop every time. Instead: define your audience, your angle, your tone, what you want included, and what you absolutely do not want. The more specific your input, the less editing your output needs.

If you are still figuring out how to write prompts that actually work, we have a complete guide for that.

3. Add What AI Cannot

AI does not have experiences. You do. AI has never been embarrassed in a meeting, never struggled with a tool, never had a customer yell at them. Your stories, your failures, your “here is what actually happened” — that is what makes content worth reading.

4. Fact-Check Everything

AI hallucinates. Confidently. It will cite studies that do not exist, quote people who never said that, and present statistics it invented on the spot. If you do not verify, you are publishing fiction labeled as fact.

We wrote an entire article about why AI makes stuff up and how to catch it. Worth reading before you publish anything AI-assisted. And if you want to know which AI hallucinates the least, we tested all of them.

5. Delete the Filler

Go through your AI output with a machete. Every “it’s important to note” — gone. Every “in conclusion” that just restates the intro — gone. Every paragraph that exists only because the AI wanted to hit a word count — gone. Your readers will thank you.

6. Ask the Mirror Test

Before publishing, ask yourself: “Would I put my name on this if everyone knew AI helped write it?” If the answer is no, it is not ready. If the answer is “I would rather not think about it” — it is definitely not ready.

What the Big Platforms Are Doing

The platforms are waking up, slowly:

• Google launched its March 2024 Core Update specifically targeting “scaled content abuse” — sites mass-producing AI articles to game rankings. They reported a 45% reduction in low-quality content in search results. Google’s E-E-A-T framework now heavily rewards genuine human experience.
• Meta started labeling AI-generated images with “Made with AI” tags since April 2024, though enforcement is inconsistent at best.
• Amazon capped self-publishing to 3 titles per day and started requiring AI disclosure.
• OpenAI added C2PA watermarking metadata to DALL-E images since February 2024.
• The EU AI Act requires transparency for AI-generated content, with phased enforcement through 2026.

Translation: the days of “generate and dump” are numbered. Content that cannot prove human value will get buried.

The Silver Lining

Here is the counterintuitive truth: AI slop is actually good news for people who put in effort.

When 90% of content is generic AI filler, the remaining 10% — the stuff with real experience, real opinions, real expertise — becomes dramatically more valuable. Google is already rewarding it. Readers are already gravitating toward it. The bar for “good” just got a lot lower, because the floor for “bad” dropped through the basement.

The people who use AI as a starting point and add genuine human value? They win. The people who hit “generate” and “publish” in the same breath? They are building sandcastles at high tide.

Use the tools. Just do not let the tools use you.

Keep reading:

• AI Safety 101: Protect Your Privacy — what to watch out for when using AI tools
• ChatGPT vs Claude vs Gemini — which AI is right for you
• Best AI Writing Tools for Beginners — tools that help you write better, not worse

Disclaimer: This article is for educational purposes only. AI tools and platform policies change frequently — verify current information before making decisions. See our full terms.

The post AI Slop: Why 90% of AI Content Is Garbage (And How to Not Add to the Pile) appeared first on DumbItDownAI.

Not all AI models hallucinate equally. Some are more careful. Some are more confident. Some just make stuff up more often than others. Here is what the actual benchmarks say — no hype, no Silicon Valley PR speak.

The Current Ranking — Updated September 2026

The independent Vectara HHEM leaderboard measures how often a model invents facts when summarizing — tested on 7,700+ real documents across news, science, medicine, law and business. The lowest hallucination rates right now:

Model	Hallucination rate
finix_s1_32b (Ant Group)	1.8%
GPT-5.4 nano (OpenAI)	3.1%
Gemini 2.5 Flash-Lite (Google)	3.3%
Phi-4 (Microsoft)	3.7%
Llama 3.3 70B (Meta)	4.1%
Mistral Large (Mistral)	4.5%
Qwen3-8B (Alibaba)	4.8%

The short answer: among the big-name providers, OpenAI’s GPT-5.4 and Google’s Gemini 2.5 currently lead this summarization benchmark, with open-source models like Llama and Qwen right behind. One catch: this score only measures summaries. Claude’s real strength — admitting when it does not know — is not captured by this single number (more on that below). Source: Vectara HHEM-2.3 leaderboard.

Last updated: September 2026. We refresh this comparison every quarter — next planned update: Q4 2026. The AI market shifts monthly; older percentages always refer to the model version tested at the time.

How this ranking is put together

We don’t invent our own numbers — we pull together respected independent hallucination benchmarks and explain them in plain English. The table above is based on the Vectara HHEM leaderboard (current generation, November 2025): more than 7,700 real documents across 10 fields — law, medicine, finance, science, technology, business, politics, sports, education and stocks — with texts up to 32,000 tokens, several dozen book pages. It measures how often a model invents facts that aren’t in the source when summarizing.

We also add benchmarks that test other kinds of error — like whether a model admits when it doesn’t know (see the box “Why a low number can be misleading”). Every figure is tied to the specific model version tested, with a date and a source, so you can check it yourself.

Why a low number can be misleading

The top spots on the summary ranking currently go to small or specialized models. The big-name flagships actually score higher on the same Vectara test: Gemini 3.1 Pro at 10.4%, Claude Sonnet 4.6 at 10.6%, and the smaller Claude Haiku 4.5 at 9.8% (as of August 2026).

And there’s a completely different kind of error: the AA-Omniscience benchmark (August 2026) doesn’t test summarizing — it tests what a model does when it doesn’t know the answer to a knowledge question: admit it, or confidently guess. The result is sobering — depending on the model, the top models confidently gave a wrong answer in roughly a third to over 90 percent of those cases instead of admitting they didn’t know. Even models usually considered especially cautious don’t automatically do well on this test. The exact percentages vary by analysis, so we deliberately don’t list individual numbers here. The takeaway: there is no single hallucination rate — it depends on which task you measure.

Why There Is No Single “Hallucination Score”

This is the most important thing to understand before we look at any numbers: a model can be extremely reliable at math but completely unreliable at citing sources. Or vice versa.

That is why serious benchmarks do not just measure “hallucinates yes/no.” They test different types of tasks:

• Summaries — does the AI invent details not in the original text?
• Factual knowledge — are numbers, dates, and names correct?
• Unanswerable questions — does the AI admit when it does not know?
• Medical questions — how dangerous are the errors?
• Long documents — does information get lost or changed?
• Sources and citations — do the referenced studies actually exist?

So “which AI is most reliable” depends entirely on what you are asking it to do.

The Big 4 Compared

Claude (Anthropic) — The Careful One

Claude models consistently stand out for one thing: in everyday, conversational use, they are more likely to say “I am not sure” instead of guessing.

Where it shines:

• Lower hallucination rates in summaries
• More honest about uncertainty
• Strong in medical contexts (according to MedRxiv studies)
• Less “overconfidence” — fewer self-assured wrong answers

Where it struggles:

• Can be too cautious — sometimes refuses answers it could actually give
• Less “bold” in creative tasks

Best for: Research, fact-checking, medical questions, anything where accuracy beats creativity.

ChatGPT / GPT-5 / o-Models (OpenAI) — The All-Rounder

OpenAI now offers different model types: GPT-5 for general tasks and the o-models for complex reasoning.

Where it shines:

• GPT-5 hallucinates significantly less than GPT-4o — measurably so
• The o-models are particularly strong at logic and multi-step thinking
• Excellent language quality and structure
• Strong coding abilities

Where it struggles:

• OpenAI researchers themselves admit: training methods tend to motivate guessing over honest “I do not know” responses
• Still prone to inventing sources and citations

Best for: Logic tasks, programming, structured writing, everyday tasks.

Gemini (Google) — The Multitasker

Google Gemini hits top scores in many benchmarks, especially for multimodal tasks (text + image + code).

Where it shines:

• Extremely strong at multimodal tasks
• Good at math
• Huge context window — can process very long documents
• Live web access reduces hallucinations about current events

Where it struggles:

• Tendency toward “overconfidence” — answers questions wrong rather than admitting uncertainty
• Performance varies significantly by task type

Best for: Image analysis, long documents, multimodal tasks, current research with web search.

Grok (xAI) — The Risky One

Grok positions itself as the less filtered model. That has consequences for reliability.

Where it shines:

• Fewer content restrictions
• Integrated into X (Twitter)

Where it struggles:

• Higher hallucination rates than competitors in benchmark tests
• Less research transparency
• Quality varies between versions

Best for: Unfiltered conversation, social media context. For fact-based work, not your first choice.

The Side-by-Side Comparison

How to read this: Green = reliable in benchmarks, Amber = decent but with gaps, Red = be extra careful here.

How do you even measure a hallucination?

The obvious question: if an AI makes something up, who catches it, and how does that turn into a percentage? Checking every answer by hand would be impossible. The trick: a second, specialized AI acts as the referee.

On the Vectara test it works in three steps:

1. The model being tested is given a source text and asked to summarize it.
2. A dedicated evaluation model (HHEM) compares the summary against the original and assigns a score between 0 and 1: how likely is it, in the evaluation model’s judgment, that the summary as a whole is supported by the original? If the score drops below 0.5, the summary counts as a hallucination.
3. This is repeated across more than 7,700 texts, and the share of summaries that fall below 0.5 is the hallucination rate in percent.

The key thing to understand: what’s measured is “invented compared to a given source” — not “is the AI right about the world.” That’s exactly why summaries work so well as a test: there’s a fixed yardstick (the source text) to check every claim against. The flip side: the test only tells you about summarizing — it doesn’t automatically prove a model invents little on open knowledge questions.

Other benchmarks measure differently: TruthfulQA presents models with a catalog of common human misconceptions and checks whether they fall for them. DefAn asks questions that each have one definitive, checkable answer — a date, a number, a name — and counts how often a model states something false anyway.

What the Numbers Actually Say

Short summaries: under 5% hallucination rate for the leaders. As the leaderboard above shows, the top models only invent details in roughly 2 to 5 percent of short summaries. Sounds good — but if you do 100 summaries a day, that is still up to 5 invented facts.

Unanswerable questions: 50-90% failure rate. When models get deliberately trick questions, many still produce an answer 50 to 90 percent of the time — instead of honestly saying “I do not know.” Claude tends to do better here because it is more willing to decline.

Medical questions: 20-80% depending on the task. Simple diagnosis questions work OK. Lab data interpretation and chronological reasoning? Hallucination rates skyrocket to 80 percent and beyond.

Long documents: Silent errors. Microsoft researchers found that current AI models introduce “substantial errors” when editing long documents. Information gets lost, details change — and the model does not mention it.

The Golden Rule: Cross-Check What Matters

The honest answer to “which AI hallucinates the least?” is: it depends on what you are asking.

That is why the best strategy is not finding the perfect model — it is using multiple models to check each other.

And here is the thing: you do not need to do this for every single AI interaction. Writing emails, brainstorming ideas, rephrasing text — just use one tool and go. The cross-check is only for moments when you are taking AI answers as facts that will affect real decisions. Health, legal, financial, scientific stuff. That is it.

No fear. Just common sense when it counts.

Related Articles on DumbItDownAI

• AI Slop: Why 90% of AI Content Is Garbage (And How to Not Add to the Pile)
• AI Hallucinations: Why Your AI Sometimes Makes Stuff Up
• AI Safety 101: Protect Your Privacy
• ChatGPT vs Claude vs Gemini: Which Free AI?
• How to Write AI Prompts That Actually Work

Want to create AI explainer videos without a camera? Fliki turns your text into professional videos — perfect for breaking down complex topics like this one.

Cite this analysis

Writing about AI reliability and want to link? Go ahead — here’s a ready-made snippet:

“According to DumbItDown’s hallucination comparison (updated September 2026), the best small or specialized models hallucinate in under 4% of summaries, while well-known flagship models sit around 10%. On knowledge questions they can’t answer, even top models frequently guess instead of admitting they don’t know. There is no single, universal hallucination rate — it depends on the task.”

Source: DumbItDownAI — Which AI Hallucinates the Least?

The Bottom Line

No model is hallucination-proof. But there are clear differences: on the current Vectara benchmark, GPT-5.4 and Gemini 2.5 post the lowest summary hallucination rates; Claude is the quickest to admit uncertainty in everyday use, ChatGPT is strongest at logic, Gemini handles long documents and multimodal tasks best, Grok is the riskiest.

But honestly, the most important takeaway from all these benchmarks is not which model wins — it is that you should never rely on just one AI for anything that actually matters. Two minutes with a second model can save you hours of trouble.

Use AI. Love AI. Just double-check the important stuff. That is the whole secret.

Frequently Asked Questions

Which AI hallucinates the least in 2026?

On the independent Vectara HHEM leaderboard (current generation, 2026), the lowest summary hallucination rates among well-known providers are OpenAI’s GPT-5.4 nano (3.1%) and Google’s Gemini 2.5 Flash-Lite (3.3%). The open-source model finix_s1_32b scores lowest overall at 1.8%. This benchmark measures factual accuracy in summaries only.

Is ChatGPT or Claude more accurate?

It depends on the task. ChatGPT (GPT-5) scores marginally lower on the summary hallucination benchmark, while Claude is the most likely to admit uncertainty instead of guessing in everyday use — which makes it more reliable for research, medical and fact-checking questions where a confident wrong answer is dangerous.

Which AI is best for fact-checking?

Claude, because it more often says “I’m not sure” instead of inventing an answer. For anything critical, the safest method is to cross-check the same question across ChatGPT, Claude and Gemini: if all three agree it is probably correct; if they disagree, verify the specific point that differs.

Does Gemini hallucinate more than ChatGPT?

On summaries the two are close, with GPT-5.4 slightly ahead. Gemini’s weakness is overconfidence — it tends to answer uncertain questions rather than admit it does not know — but its live web access and large context window make it strong for current events and long documents.

How do I stop AI from making things up?

You cannot fully stop it — no model is hallucination-proof. The practical fix is the 2-minute cross-check: ask the same question in two or three different AI models and only trust the parts they agree on. Reserve this for facts that affect real decisions (health, legal, financial, scientific).

The post Which AI Hallucinates the Least? The Honest Comparison 2026 appeared first on DumbItDownAI.

Welcome to the wonderful world of AI hallucinations. It sounds dramatic, but once you understand what is happening, it is actually pretty easy to deal with. No panic required.

What Is an AI Hallucination?

An AI hallucination is when your AI makes something up and presents it as fact. Not a typo. Not a misunderstanding. It straight-up invents information and delivers it with zero hesitation.

This can look like:

• Invented facts — statistics, dates, and numbers that do not exist
• Fake sources — studies with real-sounding author names but completely made-up content
• Constructed connections — logical-sounding explanations that are just not true
• Absolute confidence — no “maybe”, no “I think”, just stated as gospel

The tricky part: the better the AI writes, the harder it is to spot. The answer reads like a textbook. It is just that the textbook does not exist.

Why Does This Happen?

Here is the thing most people do not realize: AI language models do not “know” things. They calculate probabilities. They have learned which words tend to follow which other words, what answers typically look like, and what structure sounds convincing.

That is the difference between “writing well” and “knowing the truth.” Your AI is really, really good at the first one. The second one? That is your job.

The 5 Most Common Triggers

1. Missing information
When the AI does not have the answer, it fills the gap with whatever sounds most plausible. Instead of “I do not know,” you get a confident-sounding guess.

2. Vague questions
“Tell me about cancer” is basically an invitation for the AI to start making things up. The less specific your question, the more room for creative fiction.

3. Things that do not exist
Ask about a book that was never written, and the AI will invent it — complete with author, publisher, and page count. Impressive, honestly. Just not helpful.

4. Outdated training data
Every model has a knowledge cutoff date. Everything after that is a gray zone — and the AI guesses instead of admitting it.

5. Complex specialized topics
Medicine, law, science, finance — these are where hallucinations get really dangerous. Studies show hallucination rates of 20 to over 80 percent for medical questions, depending on the task type.

Quick Clarification: Mistakes Are Not Hallucinations

Not every AI error is a hallucination. There is a difference:

• Regular mistake: The AI miscalculates, misunderstands your question, or phrases something badly
• Hallucination: The AI invents content and presents it as verified fact

A math error is annoying. A fake study that you cite in your thesis is a career problem.

5 Warning Signs That Your AI Is Hallucinating

Warning Sign 1: Over-the-Top Confidence

Words like “definitely,” “guaranteed,” or “all experts agree” — without a single verifiable source. Real experts are usually more cautious than a hallucinating AI.

Warning Sign 2: Sources You Cannot Find

Studies with untraceable authors, links that go nowhere, books with oddly specific titles. If you cannot Google the source in 30 seconds, it probably does not exist.

Warning Sign 3: Suspiciously Perfect Answers

The AI answers every question instantly, never shows doubt, and knows every number exactly. That is not competence — that is guessing with style.

Warning Sign 4: Contradictions

The AI says one thing in response 3 and something different in response 1. This happens a lot with hallucinations because made-up facts are hard to keep track of — even for an AI.

Warning Sign 5: Smooth but Empty

The answer sounds smart and professional but contains no concrete, verifiable details. Lots of words, little substance. We have all met that person at a networking event.

Do Not Panic — But Do Use Common Sense

OK so now you have read a lot about invented facts and dangerous errors. Maybe you are thinking: should I even use AI at all?

Yes. Absolutely.

Here is what matters: hallucinations are only a real problem when important topics are involved. If you ask the AI to write a birthday speech, brainstorm project ideas, or rephrase an email — it literally does not matter if the AI makes something up. Those are creative tasks. It is allowed to invent things there.

It only gets critical when you take AI answers as facts — especially for:

• Health and medicine
• Legal questions and contracts
• Finance and taxes
• Science and citations
• Decisions with real consequences

For those situations, there is one simple strategy that takes about 2 minutes:

Prompts That Reduce Hallucinations

You can actually make the AI less likely to hallucinate just by how you ask. Three prompt add-ons that work immediately:

Add-on 1: Ask for honesty
Add to your prompt: “If you are not sure about something, say so honestly instead of guessing.”

Add-on 2: Demand sources
“Only include information you can back up with a verifiable source. If you do not have a source, leave it out.”

Add-on 3: Separate facts from opinions
“Clearly separate verified facts from your own estimates. Label anything uncertain as a guess.”

These work in any AI — ChatGPT, Claude, Gemini, Copilot. You can use them individually or combine them. The more you include, the more careful the AI becomes.

For everyday tasks, you do not need any of this. Just type away. But when you are dealing with something where a mistake has consequences — those 30 extra seconds are the difference between “well used” and “expensive lesson.”

Where Hallucinations Get Actually Dangerous

This is not just theoretical. There have been real cases:

• A lawyer in New York submitted court filings based on completely invented case law — generated by ChatGPT
• Students cited scientific papers that never existed in their theses
• Developers shipped non-existent code functions from AI suggestions into production

So yes, hallucinations matter. But the fix is not “stop using AI.” The fix is “check the important stuff.” That is literally it.

Related Articles on DumbItDownAI

• AI Slop: Why 90% of AI Content Is Garbage (And How to Not Add to the Pile)
• Which AI Hallucinates the Least? The Honest Comparison 2026
• AI Safety 101: Protect Your Privacy
• ChatGPT vs Claude vs Gemini: Which Free AI?
• How to Write AI Prompts That Actually Work

Want to turn complex AI topics into simple explainer videos? Fliki lets you create professional videos from text — no camera, no editing skills needed.

The Bottom Line

AI hallucinations are not a bug someone forgot to fix. They are a fundamental feature of how language models work — they calculate probabilities, not truth.

The good news: models are getting better. Hallucination rates are dropping with every new generation. And with the right habits — precise questions, asking for sources, cross-checking what matters — you can reduce the risk to almost nothing.

For 90 percent of what you do with AI, hallucinations are a non-issue. For the other 10 percent, two minutes of checking makes you smarter than most AI users on the planet.

No fear required. Just a little common sense.

Related: Prompt Injection Explained — how hidden instructions can hijack an AI, and how to protect yourself.

The post AI Hallucinations: Why Your AI Sometimes Makes Stuff Up (And How to Catch It) appeared first on DumbItDownAI.

AI tools are incredibly useful. They are also incredibly curious about your data. Every time you type something into ChatGPT, Claude, or any AI tool, that data goes somewhere. Here is how to use them without accidentally sharing your life story.

The Golden Rule: Do Not Share What You Would Not Post Publicly

This is the single most important thing to remember. AI chatbots are not private journals. Treat every conversation as if it could theoretically be seen by someone else.

What You Should NEVER Put Into an AI Tool

• Passwords or login credentials – never, under any circumstances
• Social Security numbers, tax IDs, or government IDs
• Credit card or bank account numbers
• Private medical information (unless using a HIPAA-compliant tool)
• Confidential work documents – check your employer AI policy first
• Other people personal information without their consent

Understanding Data Training

Many AI companies use your conversations to improve their models. This means your input might be reviewed by employees or used in future training data. In other words, that heartfelt 2 AM message about your relationship problems might end up teaching the next AI model how to discuss heartbreak. Here is how the major tools handle this:

• ChatGPT: Uses your data for training by default – you can opt out in Settings, Data Controls
• Claude: Does not use your conversations for training by default
• Google Gemini: Check your Google account privacy settings carefully

Action step: Go into your AI tool settings right now and check the data/privacy options. Turn off training data sharing if you are uncomfortable with it. Not sure which AI to pick? Our ChatGPT vs Claude vs Gemini comparison covers the privacy differences in detail.

5 Practical Safety Tips

1. Use anonymous details. Instead of “My employee John Smith at 123 Main St,” say “My employee at the downtown office.” You get the same AI output without exposing real information.
2. Create a separate email. Sign up for AI tools with a dedicated email address, not your primary one.
3. Review before sharing. Before pasting a document into AI, scan it for personal data, client names, or confidential figures. Remove or anonymize them first.
4. Use incognito/temporary chats. ChatGPT offers a Temporary Chat mode that is not saved or used for training. Use it for anything sensitive.
5. Check your chat history. Periodically review and delete old AI conversations that contain information you would not want stored long-term.

Bonus tip: If you really want to level up your privacy game, consider using a VPN when working with AI tools. It masks your IP address and encrypts your connection — which means your internet provider (and anyone else snooping) cannot see what you are doing. Think of it as closing the blinds before you start talking to your AI assistant. Surfshark and NordVPN are solid, affordable options that work on unlimited devices — so your laptop, phone, and that tablet you forgot you owned are all covered.

AI at Work: Extra Caution Required

If you are using AI for work, there are additional considerations:

• Check your company AI policy before using any AI tool with work data
• Use enterprise versions (like ChatGPT Enterprise or Claude for Business) which have stronger data protections
• Never upload proprietary code, client data, or financial reports to free-tier AI tools
• When in doubt, ask your IT department

The Bottom Line

AI tools are safe to use for the vast majority of everyday tasks. You just need to treat them like any other online service: be thoughtful about what you share, check your privacy settings, and use common sense.

Stay informed, stay safe, and do not let privacy concerns stop you from using tools that can genuinely make your life easier. Just maybe do not tell them your deepest secrets.

Affiliate Note: Want to keep your AI security checklist handy? Notion lets you organize your privacy settings, tool passwords, and safety notes in one secure workspace. Free to start.

Keep reading: AI safety goes beyond data — learn how AI hallucinations can mislead you and how scammers use AI to create fake profiles. New to AI? Start with our complete ChatGPT beginner’s guide.

Related: Wondering how good — and how safe — Chinese AI models really are? Read Chinese AI Models: Are DeepSeek, Qwen & Kimi Safe to Use?

The post AI Safety 101: How to Protect Your Privacy When Using AI Tools appeared first on DumbItDownAI.