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mkurman
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AI Tech Lead | MD

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MedIT One
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MedIT One
reacted to albertvillanova's post with 👍 1 day ago
🚀 New smolagents update: Safer Local Python Execution! 🦾🐍 With the latest release, we've added security checks to the local Python interpreter: every evaluation is now analyzed for dangerous builtins, modules, and functions. 🔒 Here's why this matters & what you need to know! 🧵👇 1️⃣ Why is local execution risky? ⚠️ AI agents that run arbitrary Python code can unintentionally (or maliciously) access system files, run unsafe commands, or exfiltrate data. 2️⃣ New Safety Layer in smolagents 🛡️ We now inspect every return value during execution: ✅ Allowed: Safe built-in types (e.g., numbers, strings, lists) ⛔ Blocked: Dangerous functions/modules (e.g., os.system, subprocess, exec, shutil) 3️⃣ Immediate Benefits 💡 - Prevent agents from accessing unsafe builtins - Block unauthorized file or network access - Reduce accidental security vulnerabilities 4️⃣ Security Disclaimer ⚠️ 🚨 Despite these improvements, local Python execution is NEVER 100% safe. 🚨 If you need true isolation, use a remote sandboxed executor like Docker or E2B. 5️⃣ The Best Practice: Use Sandboxed Execution 🔐 For production-grade AI agents, we strongly recommend running code in a Docker or E2B sandbox to ensure complete isolation. 6️⃣ Upgrade Now & Stay Safe! 🚀 Check out the latest smolagents release and start building safer AI agents today. 🔗 https://github.com/huggingface/smolagents What security measures do you take when running AI-generated code? Let’s discuss! 👇 #AI #smolagents #Python #Security
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Just released NVAMP Loss!

✔️ modification of the cross-entropy loss function designed specifically for training LLMs.
✔️ twist on the standard cross-entropy loss by emphasizing the importance of outlier prediction errors and dynamically normalizing token-level variance.
✔️ more stable and efficient training, leading to models that generalize better.

Check it out, give it a spin, and let me know what you think!

Licensed under the Apache 2.0 license and ready to use. Happy training! 🔥🤖

https://github.com/mkurman/nvamp-loss
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