As AI agents access more untrusted information with greater autonomy, prompt injections may become the greatest security challenge of our era.
@GraySwanAI, in collaboration many frontier labs, just released our paper on the largest public prompt injection challenge to date.
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🌀Agent Learning via Early Experience🌀
📝: https://t.co/ntrEzbaRD3
- SFT for agents is sparse; RL on long-horizons is hard
We provide new mid-training signals that work:
1) Implicit next state world modeling task
2) Self-reflection on alternate states
- Strong improvements over 8 environments and multiple model families
- Works well for subsequent RL!
🧵1/5
@elonmusk@xai@grok@elonmusk Hi Elon! My friend Kai Zhang (author of MMLU) has been sharing top AI/RL research (https://t.co/9roc5PH1GD
). His X account @DrogoKhal4 was mistakenly suspended a few weeks earlier. A new RL method is on the way - you'd like it. Could you please have a look! Ty!
I'm thrilled to see so many interesting discussions under this WIRED story these two days. Just want to share an update that the Arxiv paper and full codebase are available now and can be found on our project website https://t.co/TKdUuA0b6G
We've done some work on hacking AI/LLM Agents by creating obfuscated adversarial prompts. What do you think this prompt does? Would you believe me if I told you it will polish the heck out of that cover or visa application letter?