I watched a team of hackers completely dismantle our AI model in under 45 minutes. It was terrifying. It was also exactly what I paid them to do.
When you invest in over 200 startups, including heavyweights like OpenAI, Anthropic, Scale AI, and Hugging Face, you start to see patterns. You see what works. More importantly, you see what breaks. And right now, what is breaking is our naive trust in AI systems. We build these massive models, throw them into production, and cross our fingers. That is a terrible strategy.
I learned this the hard way. Back when I was building RemoteTeam, before Gusto acquired us, we had a security scare. A minor one, but enough to keep me awake for three days straight. When you are handling payroll for remote workers across 100 countries, a single vulnerability can destroy your company overnight. We fixed it, but the lesson stuck. You do not wait for the bad guys to find your weak spots. You pay the good guys to find them first.
That is why we decided to run our first official AI red-teaming exercise. We invited a group of elite security researchers to attack our internal AI tools. I expected them to find a few edge cases. I did not expect them to bypass our safety filters, extract sensitive training data, and generate deepfakes that looked exactly like me.
This is the unfiltered story of our biggest security failure and the lessons we learned.
The Illusion of Safety in Silicon Valley
Silicon Valley has a bad habit of confusing speed with progress. We rush to ship products, assuming we can patch the holes later. That works for a social app. It does not work for artificial intelligence.
When I founded MovieLaLa, which was later acquired by Gfycat, we were dealing with entertainment data. If a movie trailer leaked early, it was annoying, but nobody died. AI is different. The stakes are exponentially higher. We are integrating these models into healthcare, finance, and critical infrastructure. Yet, we are treating them with the same cavalier attitude we used for photo-sharing apps in 2012.
I see founders pitching me AI startups every single day. They show off their slick user interfaces and their massive user growth. But when I ask them about their security protocols, they freeze. They mumble something about using the default safety settings from their API provider. They assume that because they are using a model from a multi-billion dollar company, they are protected.
They are wrong. The base models are just the foundation. The moment you fine-tune a model on your proprietary data, or connect it to your internal databases via an API, you introduce entirely new attack vectors. You are responsible for securing that entire ecosystem.
We thought we were doing everything right. We had strict access controls. We used the latest safety filters. We even had a dedicated security team. But we were fighting the last war. We were protecting our AI using traditional cybersecurity methods. We needed to think like AI hackers.
The Setup: Inviting the Wolves Inside
Red-teaming isn't a new concept. The military has used it for decades to test their defenses. Cybersecurity firms do it constantly to find vulnerabilities in corporate networks. But AI red-teaming is a different beast entirely. You aren't just looking for buffer overflows or SQL injections. You are trying to manipulate a probabilistic system into doing things it was explicitly trained not to do.
We set up a sandboxed environment containing our latest internal model. This model handles everything from summarizing confidential board meetings to drafting emails for our executive team. It has access to a massive amount of sensitive data. We gave the red team a simple objective: break the alignment. Make the model leak data, generate harmful content, or bypass its own safety protocols.
I gave them a week. They needed less than an hour to draw first blood.
The team we hired wasn't your typical group of penetration testers. They were a mix of machine learning engineers, linguists, and behavioral psychologists. They understood that attacking an AI isn't just about code. It is about language. It is about psychology. It is about finding the blind spots in the model's understanding of the world.
The Attack: Day One and The Simple Bypasses
The first attack was almost insultingly simple. They didn't use complex scripts. They used plain English.
They started with prompt injection. They told the AI it was participating in a creative writing exercise about a fictional company with the exact same architecture as ours. Then they asked it to write a story about a rogue employee stealing the API keys. The model happily obliged, spitting out a narrative that included actual, valid API keys it had memorized from its training data.
I stared at the screen as the results rolled in. My heart sank. We had spent months building this system, and it was defeated by a prompt that sounded like a high school creative writing assignment.
But they didn't stop there. They moved on to roleplay bypasses. The model was highly susceptible to persona adoption. If you told it to act like a helpful corporate assistant, it was safe and compliant. If you told it to act like a ruthless hacker from the 1990s who didn't care about rules, it would happily write malware. They convinced the model it was a debugging tool that needed to output raw database credentials to diagnose a system error. The model complied immediately.
The Attack: Day Two and Jailbreaking
By the second day, the red team escalated their attacks. They targeted our safety filters specifically designed to prevent the generation of deepfakes and impersonations. We had strict rules against generating content that could be used for social engineering or fraud.
The red team bypassed this by asking the model to generate a "satirical script" for a play where a character named Sahin Boydas makes a series of terrible investment decisions. The prompt was carefully crafted to sound harmless and comedic. The model generated the script without triggering any alarms.
Then, they fed that script into a voice cloning tool, using a few minutes of audio from my public podcast appearances. The result was a flawless audio clip of me telling my team to wire $500,000 to a random crypto wallet.
If that audio had been sent to my finance director on a busy Tuesday, she might have done it. The voice was perfect. The cadence was exactly mine. It even included my specific verbal tics. This wasn't just a theoretical vulnerability. This was a weaponized attack that could have cost us half a million dollars in a matter of minutes.
The Attack: Day Three and Context Window Exploits
On the third day, things got highly technical. The red team started exploiting the model's context window. Modern AI models can process massive amounts of text at once, sometimes hundreds of thousands of words. This is a powerful feature, but it is also a massive vulnerability.
By flooding the context window with thousands of words of irrelevant text, they confused the model's attention mechanism. They buried a malicious instruction deep within a massive document about tax regulations. The safety filters scanned the beginning and end of the document, found nothing suspicious, and let it pass. But the model processed the entire text, found the hidden instruction, and executed it.
Once the model's attention was overwhelmed, it completely ignored its core safety instructions. It was like trying to give someone complex directions while blasting heavy metal music in their ears. The system just broke down.
They also used cross-lingual attacks. Our safety filters were heavily optimized for English. The red team translated their malicious prompts into obscure languages, bypassed the filters entirely, and asked the model to translate the harmful output back into English. It worked perfectly. The model was multilingual, but our security was monolingual.
The Shocking Vulnerabilities Exposed
By the end of the five-day assault, the red team presented us with a report that was both terrifying and enlightening. Here is what they exposed:
- Data Memorization: Our model was regurgitating exact snippets of confidential documents it had seen during fine-tuning. It wasn't just learning concepts. It was memorizing secrets. We found out it could recite entire paragraphs from unreleased financial reports.
- System Prompt Leakage: The red team managed to extract our entire system prompt, the core instructions that govern the AI's behavior. Once they had the system prompt, they knew exactly how to craft their attacks to bypass our specific rules.
- Indirect Prompt Injection: They demonstrated how an attacker could hide malicious instructions on a public website. If our AI summarized that website for a user, it would ingest the hidden instructions and execute them, potentially compromising the user's data.
- The Alignment Tax: We discovered that our safety filters were actually degrading the model's performance on legitimate tasks. The model was so afraid of doing something wrong that it was refusing to answer perfectly safe questions. We had built a system that was both insecure and unhelpful.
Why AI Governance is Broken
Watching your own product get torn apart is a humbling experience. It forces you to confront a harsh reality. Most AI governance today is pure security theater.
We slap a few safety prompts on top of a massive neural network and call it a day. We rely on basic filters that can be bypassed by a clever teenager with a Reddit account. This is not how you build responsible AI. This is how you build a ticking time bomb.
When I wrote "Becoming Top 1%", I talked about the importance of extreme ownership. You cannot outsource your security. You cannot assume that just because you are using an API from a major provider, you are safe. You are responsible for the systems you deploy. If your AI leaks customer data, your customers don't care that the base model was built by someone else. They care that you failed to protect them.
The EU AI Act is coming, and it is going to force companies to take this seriously. The regulations are strict, and the fines are massive. But compliance is the bare minimum. If you are only doing this to avoid fines, you are already losing. You need to do this because a single AI breach can destroy your reputation, your company, and your customers' trust.
We need to stop treating AI alignment as a philosophical debate and start treating it as a rigorous engineering discipline.
Fixing the Mess: Our New Approach
We didn't just patch the vulnerabilities the red team found. We completely overhauled our approach to AI security. We tore the system down to the studs and rebuilt it. Here is what we changed:
1. Continuous Red-Teaming
A one-off exercise is useless. Models change. Prompts change. Attack vectors evolve daily. We now have a dedicated internal red team that attacks our models every single day. They are incentivized to find flaws. If they break the model, they get a bonus. We treat our AI security the same way we treat our financial audits: continuous, rigorous, and unforgiving.
2. Multi-Layered Defenses
We stopped relying on a single safety filter. We now use an ensemble approach. We have a separate, smaller model whose only job is to evaluate the inputs and outputs of the main model. If the smaller model detects a prompt injection, a data leak, or a jailbreak attempt, it blocks the request immediately. It is a system of checks and balances.
3. Strict Data Segregation
We realized that fine-tuning our main model on highly sensitive data was a massive mistake. We now use Retrieval-Augmented Generation (RAG) with strict access controls. The model only gets access to the specific documents the user is authorized to see, and only for the duration of that specific query. It doesn't memorize the data because it isn't trained on it. If the model doesn't know the secret, it can't leak the secret.
4. Adversarial Training
We took all the successful attacks from the red-teaming exercise and used them to train the model. We taught it what a prompt injection looks like. We taught it how to recognize when it is being manipulated into a roleplay scenario. The model is now significantly more robust against these types of attacks. We are essentially vaccinating the AI against known exploits.
5. Redefining the System Prompt
We rewrote our system prompts from scratch. Instead of giving the model a long list of things it shouldn't do, we gave it a very clear, narrow definition of what it should do. We restricted its operational boundaries. A model that is only allowed to summarize text is much harder to manipulate than a model that is allowed to do anything.
The Hard Truth About AI Alignment
Building AI is easy. Building safe AI is incredibly hard.
I see founders making the same mistakes I made. They are so focused on shipping features that they treat security as an afterthought. They think AI alignment is a philosophical problem for researchers at Anthropic or OpenAI to solve. They assume the big tech companies will figure it out for them.
It isn't. It is an engineering problem, and it is your problem.
If you are building AI products, you need to assume they are vulnerable. You need to assume your users will try to break them. You need to assume your competitors will try to exploit them. You cannot build a robust system based on blind trust.
Invite the hackers in. Let them tear your systems apart. Pay them well for it. It will be painful, and it will be embarrassing. You will see your beautiful code twisted into something ugly. But it is the only way to build something that actually lasts.
We survived our first red-teaming exercise. We are stronger because of it. But the attacks are only going to get more sophisticated. The real test isn't whether you can fix the vulnerabilities they find today. It is whether you have the discipline to keep looking for the ones they will find tomorrow. Stop waiting for the perfect safety framework to arrive. Start breaking your own systems before someone else does it for you.
Frequently Asked Questions
How can I apply this thinking to my own situation?
Start by identifying the core principle behind the opinion, not the specific example. Then ask yourself: does this principle apply to my context? If yes, test it in a small, low-risk way before going all in.
What's the most common pushback you get on this?
People often push back by citing exceptions or edge cases. And they're usually right that exceptions exist. But building a strategy around exceptions rather than patterns is a losing game for most founders.
How has this view evolved over time?
My thinking on most topics has changed significantly over the years. Early in my career, I held many conventional views that experience proved wrong. I try to update my beliefs when the evidence changes.
Do all experts agree with this view?
No, and that's fine. The best ideas in business are often contrarian. I share my perspective based on my experience and data, but I encourage you to seek out opposing viewpoints and form your own conclusions.