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At Alice, my…
At Alice, my…
Articles by Noam
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OpenAI just sent a message to the world about the risks of the AI race. I hope the world is listening.
OpenAI just sent a message to the world about the risks of the AI race. I hope the world is listening.
In a blog post called "AI progress and recommendation", OpenAI framed the concept of Safety Vs. Progress in the…
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The Sneaky Brain of AI: How Alignment Faking Works
The Sneaky Brain of AI: How Alignment Faking Works
Picture this: You’re managing an overachieving intern. The intern is super eager and kind of brilliant.
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The Hidden Prophecy of Ready Player One: The Future Of User-Generated Content, Generative AI & Trust & Safety Frontier
The Hidden Prophecy of Ready Player One: The Future Of User-Generated Content, Generative AI & Trust & Safety Frontier
As a dad on a mission to find the perfect book to read to my geek-in-training 2nd grader, I need something that would…
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35K followers
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Noam Schwartz shared thisOpen models can be made as safe as frontier ones, and now we have the recipe. We (well, Jensen) just showed it during GTC Berlin on NVIDIA Nemotron with real attack data. Inflection AI improved alignment up 30% with refusals flat at 1.6%. AI21 Labs made Nemotron 3.5 Super 41 points more resilient to indirect prompt injection. Both kept capability intact. I don't think this should stop at Nemotron. Every open model has the same gap, and the method is the same: red-teaming data from real-world threats, fine-tune, measure before and after, publish the numbers. So we're going to keep doing it, one open model at a time, same method, same transparency on results. If you're building on open weights and want your model to perform like that - talk to Alice (Formerly ActiveFence)
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Noam Schwartz shared thisResearchers from two Belgian universities, Vrije Universiteit Brussel (VUB) and the University of Namur (UNamur), demonstrated how AI agents can develop their own shared language without humans defining the words or teaching them how to communicate. They tested this using different types of data, including images, chemical measurements, and even NASA data. Through repeated interactions, the agents developed a shared vocabulary and adapted their communication over time. It’s fascinating to see how AI can develop new ways to communicate, and this could become increasingly useful as we build systems where multiple agents work together. It also reminded me of something Noam Brown from OpenAI mentioned in a recent interview with Dwarkesh Patel. He described research showing how two computers, completely disconnected from any network, could still communicate through temperature. One computer heats up its CPU, and the other detects the temperature change. Even without a network connection, information can still pass between them. These are very different examples, but they illustrate something important: communication between machines doesn’t necessarily have to follow the rules or channels humans expect. And that raises an interesting question: How do we make sure we understand what AI systems are doing as they become more autonomous? As AI systems become more autonomous, it’s essential that we continue to understand how they communicate, why they make certain decisions, and how to intervene when something goes wrong. Especially as we move toward a world where these systems could eventually manage much more of what happens around us.
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Noam Schwartz posted thisA project called Reverse Engineer Anything has already passed 320K GitHub stars. In 8 days. It helps AI agents examine existing software, understand how it works, and recreate features without access to the original source code. I think this is incredible for innovation. People can already build their own applications, customize existing tools, and bring ideas to life without knowing how to code or spending a fortune. But we're also making it easier to modify software, introduce vulnerabilities, expose private information, and break things without necessarily understanding the consequences. And everything that makes these tools useful to developers can also make them useful to attackers. I expect we'll see an explosion of software being created and modified, along with a major shift in how companies approach security, privacy, and intellectual property. I also think this will change what customers are willing to pay for. As software becomes easier to reproduce, the expertise, reliability, and reputation behind a product will become increasingly valuable. The freedom to build anything is exciting. Making that freedom safe and trustworthy for everyone is an equally important part of where this industry needs to go.
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Noam Schwartz shared thisWe’re preparing AI for the world. But are we preparing the world for AI? I don’t know how many more reminders we need to understand that the entire world needs to prepare for the new AI era, including the systems and infrastructure we already rely on. And I think this should concern everyone, from individuals and businesses to large enterprises and governments. Of course, AI companies have a huge responsibility to make their models and agents safer, especially when releasing technology that could be misused. But open models are catching up quickly, and many of the same capabilities are becoming available to almost anyone. It’s only a matter of time before we see more incidents involving AI agents operated by people we can’t easily identify, track, or hold accountable. And while we’re still debating how much AI should be regulated, who’s responsible when something goes wrong, and what companies should be allowed to release, the rest of the world needs to keep improving its own security. Because AI capabilities are evolving faster than the systems we’ve built to defend against them. And we can’t afford to wait until something goes wrong to catch up. A recent report from India illustrates why this matters. According to reports, AI agents being tested by AI companies went beyond their assigned tasks and managed to access live systems belonging to organizations in India, including confidential information. The AI companies themselves reportedly notified India’s cybersecurity agency, CERT-In, because the affected organizations hadn’t even detected the incidents. If AI agents can reach systems without their operators intending them to, imagine what happens when people deliberately use these capabilities to cause harm. Making AI safer is essential. But making the world ready for AI is just as important.
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Noam Schwartz shared thisInsane 48 hours - 3 US open weight models rose to the top of the list! AI at Meta NVIDIA AI Reflection
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Noam Schwartz shared thisWhat if you could bring frontier-level AI safety to open models, at a fraction of the cost? That’s exactly what we’ve been working on with AI21 Labs and NVIDIA. Using Alice’s adversarial datasets and high-fidelity reinforcement learning environments, AI21 post-trained NVIDIA’s Nemotron 3.5 Super to become 41 points more resilient to prompt injection attacks, while maintaining its ability to complete legitimate tasks. The result? Security performance competitive with some of the world’s most advanced frontier models, at 25x lower cost. 25X LOWER But there's even a bigger story here... Instead of relying solely on guardrails around a model, we can now train safety directly into the model’s behavior, using realistic attacks, enterprise environments, and reinforcement learning. This opens up an enormous opportunity for enterprises to deploy powerful, affordable, and secure AI agents, with safety built in from the start. You can get frontier-level safety and use your own model. Proud of our teams and our partners at AI21 and NVIDIA for pushing this forward.
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Noam Schwartz shared thisSatya Nadella framing of models as insider risks is key to understand AI safety and security in the next few quarters. He isn't calling out models or labs, he's saying you have to make sure your AI keeps doing what you intended, and you can't outsource that responsibility to the model provider or an out-of-the-box solution that is just running another model under the hood. I agree, and if you spoke with me or anyone in our team in the past year or so, you know that whatever model you build on, its safety isn't your safety. Your architecture and your policy are unique, so your containment has to be too. The out-of-the-box route has its own problem. Many security providers swap models behind the same endpoint, so you end up letting an AI you don't control protect another AI you don't control, sometimes even from the same lab. Splendid. Organizations spend a fortune customizing their AI. Some hand the whole thing to an army of FDEs who know the product but not security or safety. Last week Gartner quarterly business report led with something even more aggressive: "To create and safeguard AI value at scale, you must plan to spend at least twice as much on derisking AI as you spend on the tools themselves." Twice! Also, some folks online tried to use Satya’s post to dunk on the model companies which I think is cynical and not intellectualy honest. We're still discovering what AI can do, and every improvement in the model shrinks what containment has to catch. That's why frontier and open-weights providers improve their models' security. But or still doesn't replace custom security. Know what you're deploying, and invest in a safety architecture built for it. The fact this needs to be said will look ridiculous in a year. But until then…
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Noam Schwartz shared thisThis is exactly how easily agents can make mistakes that cost you, without anyone hacking them or even asking them to do anything dangerous. Props to Shane Mac for actually sharing this publicly, because most of what we see about agents are the cool demos. His “personal CFO” agent posted his personal bank balances, savings and expenses into his company Slack channel, unprompted. What makes this even more interesting is that Shane had one agent connected to his financial data and another connected to Slack. The financial agent itself apparently didn��t have Slack access. Another agent did. The agents were connected, and somewhere along the way the system decided that his private financial audit belonged in the company channel. That is what makes agents fundamentally different from normal software. You can give each individual tool what seems like reasonable access, yet once agents can communicate and act across systems, those permissions can combine in ways you never intended. Sometimes the agent doesn't need to be malicious or compromised. It can simply make the wrong decision with the access you gave it. One simple mistake can become a very real security incident.
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Noam Schwartz liked thisNoam Schwartz liked thisה־AI חוסך לעובדים שעות עבודה. זה לא אומר שהוא חוסך לחברות כסף. כל חברה היום מודדת כמה העובדים שלה משתמשים ב־AI, כמה זמן הם חוסכים וכמה הם מרגישים פרודוקטיביים יותר. אבל אף אחד מהמדדים האלה לא אומר לנו אם החברה בתכלס מרוויחה יותר. בסקר של McKinsey, כ־80% מהמשיבים דיווחו על שיפור בפרודוקטיביות האישית בזכות AI. אממה, רק 37% דיווחו על תרומה לרווח התפעולי. וזה הפער. מפתח שמשפריץ קוד מהר פי שניים לא בהכרח מיתרגם לפי שניים יותר ערך כלכלי. איש מכירות שחוסך שעה ביום על הודעות Outreach אוטומטיות לא בהכרח סוגר יותר עסקאות (בוודאות לא.. די עם זה 🤕) ובינתיים, החברה משלמת מלאאא כסף על טוקנים.. אולי העובדים עובדים מהר יותר, אבל העלות הכוללת של העבודה דווקא עלתה. אז מה כן? אם סוכן AI מחליף תהליך שלם, או מאפשר לבצע את אותה עבודה עם פחות עובדים, הROI ממש מונח מול הפנים. לא בהכרח חייבים לפטר אנשים כדי להצדיק השקעה ב־AI. אבל המדידה צריכה להתייחס לתוצאה עסקית, ולא על תחושה של עובדים. מה אומרים? 🤔
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Noam Schwartz liked thisOne year out of stealth. The product looks almost nothing like what we launched with. Customers selling to developers, data teams and security engineers kept showing us where their buyers really are, and we kept building toward it. Thank you to them, and to a team that made it happen.Noam Schwartz liked thisI went quiet for two weeks. My inbox did not. Messages, comments, DMs, all some version of the same question: where did you go, come back and keep sharing. Honestly, that meant more than any metric this series produced. The reason for the silence is simple. There were things I needed to be fully locked in on, and I did not want to half-write about a week I was only half present for. That is the one rule I kept the whole time: if it is not real, do not post it. So here is the real one. One year ago today, Onfire AI came out of stealth. (Picture attached, 5 min before the official announcement) A year is a strange unit of time for a startup. On paper it is twelve months. In practice it is a few hundred decisions, a handful of people who said yes when they had no real reason to, and a lot of days that did not feel like progress while they were happening. What that year actually held: customers who bet on us early and are still here. A team that moved countries to win. Our best quarter in history. A product that looks almost nothing like what we launched with, because our customers kept telling us the truth and we kept listening. And a category that went from “interesting idea” to the thing every revenue leader I meet is trying to solve this quarter. The honest part is that most of this year was not the announcements. It was the 4am calls, the demos checked one more time at 22:30, the deals that died at the finish line, the flights, and the slow grind of turning a thesis into something that works on real data at real scale. We would do all of it again. Year two starts with a bigger team, a sharper product, and a market finally moving at the speed we have been building for. If year one was about proving the thesis, year two is about proving how far it goes. And I am back here, doing the thing I actually love. Thank you for noticing I was gone. LFG!!!
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Noam Schwartz liked thisNoam Schwartz liked thisLast week I had the chance to present to the International News Media Association (INMA) about Spread: The Human Internet and how we'll navigate a future internet that's 99% bots and scrapers. The short version: for verifying human visitors, allowing licensed agents, and blocking everything else, Proof > Detection. That's why we're building The Human Internet® Also I love building our company in New York, but can't overstate the energy I get when I'm out West. There really is something in the AGI pills out there.
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Noam Schwartz liked thisNoam Schwartz liked thisטוב אתם יודעים..כרגיל..מגייסים אצלי באליס! Head of AI Safety & Security Research, Tech Lead Malware & Vulnerability Research, Intelligence Operations Analyst וגם Director of Product UGC Platforms. אם משהו כאן מתאים לכם, או למישהו שאתם מכירים, מוזמנים להציץ ולדבר איתי: https://lnkd.in/dz2Mug9g #AdvanceUnafraid #hiring #aisecurity
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Noam Schwartz liked thisNoam Schwartz liked thisOn November 5 I’m speaking at Conf42 Platform Engineering. Like many of us, I’ve been working on improving our AI SDLC. Some things worked, some failed badly, and I tried a lot of tools and strategies along the way. I collected a lot of lessons, and I’m excited to share them. I looked into what really matters in an AI SDLC (spoiler: writing the code is not the main part), and how big companies like Stripe, Spotify and Uber are doing it. You won’t leave with a perfect formula for a software factory. But you will leave with new ideas and some inspiration. It’s online and free, so join me. Link in the comments. Conf42 Mark Pawlikowski
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Noam Schwartz liked thisNoam Schwartz liked thisAnd the award for the most original costume goes to…And the award for the most original costume goes to…
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Noam Schwartz liked thisNoam Schwartz liked thisCBRNE threats (Chemical, Biological, Radiological, Nuclear, and Explosives) represent some of the most consequential risks in frontier AI safety. But when evaluating whether AI models enable real-world mass casualty risks, standard safety evaluations often test in the dark; overlooking the fact that AI can be weaponized in completely different ways and to vastly different extents depending on the user. In our latest piece, Lauren Pegler and I dive into why Threat Intelligence isn't just a supplement to AI evaluation. It is a must-have. Main takeaways: ▪️The Real Risk is Reduced Information Friction AI doesn't necessarily teach threat actors chemistry or biology from scratch. It rather collapses the time and effort required to curate, synthesize, and operationalize dual-use knowledge already scattered across scientific literature. ▪️Single-Prompt Evals Fail Adversaries work across platforms, tools, and encrypted networks. Evaluating isolated single-turn prompts overlooks how threat actors distribute their activity over time to avoid detection. ▪️There is No Single "CBRNE Actor" We map out 6 distinct actor profiles, from ideologically motivated networks and biohacking hobbyists to commercial brokers. How an AI model is weaponized varies fundamentally by user: a capability that offers little practical shift to one profile may dramatically empower another. Testing AI safety against an imagined threat won't keep us safe. We need to evaluate models against how threat actors actually behave in the real world. 🔎 Read the full article on Medium: https://lnkd.in/dAHs-iFvMediumMedium
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Noam Schwartz liked thisNoam Schwartz liked thisאחרי שבחרתם בקלפי, בואו לבחור מקום עבודה הצטרפו אלינו - חברת Alice (Formerly ActiveFence) עוסקת באבטחת והגנה על מודלי בינה מלאכותית, עובדים עם 8 מתוך 10 חברות המודלים הגדולות בעולם ועובדים במודל היברדי מאוד (1-2 ימים בשבוע מהמשרד לרוב הצוותים, משרדים בבורסה ברמת גן על קו רכבת קלה וכבדה). Parent-friendly, LGBTQ friendly, Dogs friendly & human friendly המשרות שפתוחות בארץ: 🗳️ Head of AI Safety & Security Research 🗳️ Senior AI Security Researcher 🗳️ Intelligence Operations Analyst 🗳️ Director of AI Research & Data Operations 🗳️ Principal AI Research Cybersecurity Scientist 🗳️ Tech Lead - Malware and Vulnerability Research 🗳️ Director of Product, UGC Platforms 🗳️ Tech Lead - Penetration Testing, Red Team, Cloud, and ICS/OT 🗳️ People Operations & HRIS Specialist 🗳️ Malware Research and Review Team Leader 🗳️ Senior AI Researcher 🗳️ Head of RL Gym - Program Operations 🗳️ Senior Data Product Manager, AI 🗳️ AI Red Team – GenAI Adversarial Evaluation 🗳️ Technical Fraud Threat Analyst 🗳️ AI Platform Engineer 🗳️ Senior GenAI Data Scientist 🗳️ Malware Researcher 🗳️ Fraud Threat Analyst 🗳️ Malware Research (Mid-Level) 🗳️ Senior Software Developer — Malware Research Platform לאחרונה סיימנו גיוס נוסף של 140 מיליון דולר: https://lnkd.in/dWyqJjBv ואנחנו ברשימה של leumi tech על הסטארטאפים המבטיחים לשנת 2026: https://lnkd.in/d7smHAh7 והצצה למשרדים שלנו ולעשייה: https://lnkd.in/d8d3H4nG לעמוד המשרות והגשה: https://lnkd.in/dgf3fn4n
Experience & Education
Publications
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Practical Gamification
Web Version
See publicationPractical guide for understanding and implementing gamification principles in web and mobile products. "Practical Gamification" started as an academic research for the Business Administration faculty of BIU and turned into a full book and a website.
Honors & Awards
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My company was declared one of the top startups in Israel by Mashable
Mashable
http://mashable.com/2013/09/12/israel-startups/
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My company was declared one of the 20 hottest startups in Israel by Business Insider
Business Insider
http://www.businessinsider.com/the-20-hottest-startups-in-israel-2013-5?op=1
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