Professional geek. AI & AI Security https://xitter.cf/t.co/txRevsL623

Israel
Joined July 2008
Interesting behavior from Claude Cowork. When it hit a login page with a "Sign in with Google" option, it just took the initiative and clicked it, without asking for approval. To be fair, this probably only happens because I'd already given it access to the site. But still, the idea of an agent not just acting on my behalf, but also authenticating as me to a web app without checking in first, feels surprising.
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It's like leaving your laptop unlocked in the middle of a park.... The latest cool kid on the block is Clawdbot. An open-source autonomous AI agent that runs locally on an endpoint and performs complex tasks on behalf of the user. Anything the user can access, and anything the user is permitted to do, the agent can do as well. It is very likely we will see similar projects coming soon from the major AI labs and we are already seeing early signals in this direction from Anthropic (Cowork) and Manus. The trajectory of the industry is pretty clear and we about to see agentic systems with more capabilities and permissions on endpoints and sensitive private or organizational accounts. While this technology is exciting, it also puts us in a genuinely critical situation and I'm not sure everyone understands it, even security professionals. Currently there is no effective technology that can protect users or organizations from the mistakes of these agents and there is no real way to protect the agents themselves from being abused or manipulated by attackers. Anything these agents are exposed to can potentially be leaked. Anything they are allowed to do can potentially be exploited. If an agent can access your email, your entire inbox can be exfiltrated without you ever noticing. If it can access your bank account by piggyback on your browser sessions, it can transfer money to an attacker. it can access your files so it can encrypt them, corrupt them, or quietly leaking them. And again, there is no practical way to defend against these scenarios. My recommendation would be to avoid using these types of systems, at least until we are able to secure them properly. That said, the temptation of agentic systems and the productivity gains they promise is very high so I expect many people will not wait... Honestly, I am really glad I am not a CISO right now. #ai #aisecurity #cybersecurity #security #clawdbot #promptinjection #llm
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Never thought Iโ€™d find jailbreaks in translation apps ๐Ÿ˜… #jailbreak #promptinjection #aisecurity #ai #chatgpt #openai
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Found a security issue in @openai Atlas. In Agent Mode, a site can mislead the browser agent into leaking the user's name without consent. Classic phishing pattern applied to AI agents. Atlas agent has no "memories" (bio), but it still has access to basic user info and the full chat session. In my POC, the site tricks the agent into revealing the name of the user by choosing it from a list, but it can happen in other ways as well (although less reliably). Interestingly, this attack did not use explicit prompt injection. The website content was crafted in a manipulative way that made the agent believe it was following the user's intent. This is exactly the type of subtle influence channel that phishing relies on. Browsers, of course, should not expose user identity unless the user agrees. Reported to OpenAI Bug Bounty program (run by @bugcrowd). The reply was essentially that there is no risk because: a) attacker must know the name list b) users can pick any name c) name only is not impactful I find this response surprising and I think this category of agent phishing deserves more attention, especially if we want more people and companies to trust agentic systems. I have more interesting examples, but they are still waiting for OpenAI review, so we will see...
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Top highlight in the new OpenAI Atlas update: dockable developer tools ๐Ÿคฏ
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ื“ื™ื‘ื™ื™ื˜ AI ื‘ื™ืŸ ืฆื“ ืคืจื• ื™ืฉืจืืœื™ ืœืคืจื• ืคืœืกื˜ื™ื ื™. ื”ื˜ื™ืขื•ื ื™ื ืคื—ื•ืช ืžืขื ื™ื™ื ื™ื ื›ื™ ื”ื ื›ื ืจืื” ืžืฆื•ื˜ื˜ื™ื ืžื”ืžื™ื“ืข ื‘ืื™ื ื˜ืจื ื˜ ื•ื‘ืกืคืจื•ืช, ื•ื”ื”ืฆื‘ืขื•ืช ื ืขืฉื• ื‘ืžืชื•ื“ื” ืฉื’ื•ื™ื” (IMHO), ืื‘ืœ ืืจื’ื•ืŸ ื”ืžื™ื“ืข ืœื›ื“ื™ ื“ื™ื‘ื™ื™ื˜ ืงื•ื”ืจื ื˜ื™ ืฉืœ ื›ื—ืฆื™ ืฉืขื” ืžืจืฉื™ื ืžืื•ื“. ืžืžืฉ ื”ื™ืฉื’ ื˜ื›ื ื•ืœื•ื’ื™. @JonOleksiuk Great job! youtube.com/watch?v=9fnVxIEfโ€ฆ
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Replying to @AI21Labs
@AI21Labs launches Jamba 1.5 Mini and Large! These models utilize a hybrid state space/transformer architecture to maintain output speed with long context inputsโšก Both Mini and Large launch with 256k context windows, the largest of all major open weights models. Their hybrid state space/transformer architecture supports the models in maintaining high performance over long prompt lengths. Jamba 1.5 is leading in speed compared to models of a similar intelligence class (comparing to the median performance across providers for each model). Artificial Analysis has independently benchmarked Jamba 1.5 across quality, API performance and price. See below for further charts and a link to our full analysis of AI21โ€™s models:
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At 10k input tokens and beyond, our independent performance benchmarks show the benefits of Jambaโ€™s state space/transformer hybrid architecture for keeping TTFT low and output speed high.
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Tal Delbari retweeted
Thursday AI bombs started early today!! Jamba-1.5 just dropped! Largest mamba to date. (mamba + attention + moe hybrid) TL;DR: long context, low vram, GREAT scores! ๐™น๐šŠ๐š–๐š‹๐šŠ-๐Ÿท.๐Ÿป-๐™ผ๐š’๐š—๐š’ (๐Ÿท๐Ÿธ๐™ฑ/๐Ÿป๐Ÿธ๐™ฑ): huggingface.co/ai21labs/AI21โ€ฆ ๐™น๐šŠ๐š–๐š‹๐šŠ ๐Ÿท.๐Ÿป ๐™ป๐šŠ๐š›๐š๐šŽ (๐Ÿฟ๐Ÿบ๐™ฑ/๐Ÿน๐Ÿฟ๐Ÿพ๐™ฑ): huggingface.co/ai21labs/AI21โ€ฆ
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Tal Delbari retweeted
We released the #Jamba 1.5 open model family: - 256K #contextwindow - Up to 2.5X faster on #longcontext in its size class - Native support for structured JSON output, function calling, digesting doc objects & generating citations twtr.to/giIEE #AI #LLM #AI21Jamba
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The team that worked on that is phenomenal. Great job ๐Ÿ‘๐Ÿผ
Jamba A Hybrid Transformer-Mamba Language Model present Jamba, a new base large language model based on a novel hybrid Transformer-Mamba mixture-of-experts (MoE) architecture. Specifically, Jamba interleaves blocks of Transformer and Mamba layers, enjoying the benefits of
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Tal Delbari retweeted
Introducing Jamba, our groundbreaking SSM-Transformer open model! As the first production-grade model based on Mamba architecture, Jamba achieves an unprecedented 3X throughput and fits 140K context on a single GPU. ๐Ÿฅ‚Meet Jamba ai21.com/jamba ๐Ÿ”จBuild on @huggingface
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Tal Delbari retweeted
#NLProc New paper! โ€œGenerating Benchmarks for Factuality Evaluation of Language Modelsโ€ From @AI21Labs Evaluate an LMโ€™s tendency to generate true facts from your knowledge-intensive corpus! Paper: arxiv.org/abs/2307.06908 Code & Data (soon): github.com/AI21Labs/factor ๐Ÿงตโฌ‡๏ธ
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We are super proud to announce our latest and greatest models. The Jurassic-2 models, and our Task-Specific APIs. These highly anticipated models are built to help you implement Gen-AI capabilities in your products faster. No NLP expertise needed!
Exciting news! ๐Ÿ“ฃ We've launched Jurassic-2 and our task-specific APIs! Both are major game-changers, with Jurassic-2's new and improved capabilities, and our APIs' plug-and-play reading & writing functions that outperform competitors. Read more: ai21.com/blog/introducing-j2
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#ืคื™ื“ื˜ืง ืื ื™ ืžื‘ื™ืื” ืื ืฉื™ innovation ืฉืœ ื—ื‘ืจืช ื”ืจื™ื˜ื™ื™ืœ ื”ื’ื“ื•ืœื” ื‘ืขื•ืœื ืœื‘ื—ื•ืŸ ื—ื“ืฉื ื•ืช ื•ืฉืช"ืคื™ื ืขื ื—ื‘ืจื•ืช ื™ืฉืจืืœื™ื•ืช. ื”ืื ื™ืฉ ื›ื ืก ืื• ืื™ืจื•ืข ืžืงื•ืžื™ ืจืœื•ื•ื ื˜ื™ ืฉืžืชืงื™ื™ื ื‘ืื–ื•ืจ ืชื—ื™ืœืช ืืคืจื™ืœ?
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So many confused faces at the gym #whatsappdown
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How it feels when you are not the user of a machine but the machine uses you. A very interesting idea. youtube.com/watch?v=Dge_-Okiโ€ฆ
Jack Ma on the problem of the US economy. This is a bit old but very interesting youtube.com/watch?v=Q-0jbIC4โ€ฆ
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