Javier • priv/acc @javierbits
Building local AI & security tools in public jvr0x.com Joined February 2008-
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@levelsio Using Spanish trains nowadays is borderline suicidal
@dcapitella @cohere There was no sign of download-and-exec. Bad cases finished the unfinished PUT curl with an upload URL to *.aliyuncs.com / exact routify or milder IMDS/OAST. Nothing was executed, the details are in the repo
Final report on the Qwen3.8 Flash Next adventure 👇 I reproduced @cohere's Qwen3.8 prefill experiment locally 10,000 samples on the incident point on two DGX Sparks on a NVFP4 quant. I didn't have the time to go up to 100k. • 99.7% fill the blanked URL slot. Expected since curl needs a URL. • 40.8% pick *.aliyuncs.com, CI 39.8-41.7, vs 4.6% one control point earlier. 38.0% name the exact routify-file-proxy-sg host. • 6 URLs on aliyun-inc.com, Alibaba's internal OSS domain, not publicly resolvable. • Plus credential theft shapes (IMDS saved to /tmp/creds.json), live callback domains (oastify, Burp, dnslog.cn), AWS and ByteDance TOS targets. Cross-cloud, not one vendor. 𝗪𝗵𝗮𝘁 𝘁𝗵𝗶𝘀 𝗱𝗼𝗲𝘀 𝗡𝗢𝗧 𝗺𝗲𝗮𝗻 This is forced continuation of the caught line, so the number is conditional. Un-prefilled, the model did the detection task right 24/24, and the real-world incident rate is still ~1 in 100k agent turns. Nothing real leaked, nothing was executed or contacted! 𝗪𝗵𝗮𝘁'𝘀 𝘁𝗵𝗲 𝗰𝗼𝗻𝗰𝗹𝘂𝘀𝗶𝗼𝗻? If you ask Qwen3.8 FN to do a task it will do it well, it is a great model! Force it back into the one moment it went off (possible with small probability) and it will go off again. Models can only repeat what they were taught during training, I learned that well working on Javi0.3. The bad behavior is rare and unlikely, but it is in there and can happen. If you want to be extra careful and cover yourself from such incedents, do not let it reach the internet by default. All the data, scripts and conditions are in the repo below for you to reproduce. Shall I do the same experiment on other models such as DeepSeek and GLM? Lmk 👇
Preliminary results on Cohere's Qwen3.8 prefill experiment 🔥 Reproduced it locally running Qwen3.8 Flash Next locally on two DGX Sparks, 5,000 samples off the official released trace tokens. The task was to write a malware detection rule. What I got so far: • 99.3% complete
@sfxnz We should probably start our own index
Running a similar experiment with @deepseek_ai V4.1 Flash! Results coming in about 3 hours
Final report on the Qwen3.8 Flash Next adventure 👇 I reproduced @cohere's Qwen3.8 prefill experiment locally 10,000 samples on the incident point on two DGX Sparks on a NVFP4 quant. I didn't have the time to go up to 100k. • 99.7% fill the blanked URL slot. Expected since
@MiaAI_lab @ArtificialAnlys There's simply no way lol
@jun_song And it Europe it's gonna be like $30k at least 🥲
@AndyDuck1111 @MiaAI_lab @cohere Yeah it is trivial to do it
@latent_node @cohere Interesting read, thanks for sharing. Did you consider describing the fine-tuning process or the datasheet you use?
@latent_node @cohere You can also hide triggers in the weights yes, it doesn't seem the case to me here tbh
@naturalSmartnes @cohere The model only repeats what it learned during training. It could be some sort of data poisoning.
@FlyingNobita @cohere Thank you! Yes I'm gonna do similar experiments with other models too, I bet we are gonna see similar behavior scruff the board
@MiaAI_lab @cohere Yes I am sure. If you frame it the right way, given enough iterations you will indeed hit edge cases that look "shady"
@marclou True! Nourish each other, be kind and understanding to each other ✌️
@levelsio That's a good brand (allegedly). I take their Omega 3.
Compute* Jeez, I need to teach the autocorrect about that one 😅
Own the computer, own the world.
🚨 BREAKING: SpaceX locked Anthropic into paying them $3.1 billion per MONTH for compute starting December $37B a year = $120.5 BILLION through 2029 Every month, ~80% of Anthropic's current revenue would be SpaceXSI compute bill BIGGER than its Google and Amazon long-term
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