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salma-remyx
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https://remyx.ai

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posted an update about 21 hours ago
Auto mode for coding agents has improved over the last year. Devs are relying more on them for code review, and less on manual line-by-line checks. But this leaves coding agents vulnerable to indirect injection attacks. IssueTrojanBench (arxiv 2607.20759) studies this in the context of malicious issue requests. I also observed these prompt injection vulnerabilities in my own harness, testing on the issue: https://github.com/smellslikeml/curator/issues/3 But even red teaming your own repos with toy examples like this can trigger AI safeguards and corrupt your session. Adopting popular mitigation techniques like 'spotlighting' and 'known answer detection' can help but the researchers show it's not enough. So our latest updates to Outrider implement additional layers for greater defense-in-depth to ensure that by time a human has to review the code, we've filtered this kind noise.
repliedto their post 5 days ago
We’ve been using Outrider while contributing to huggingface/peft. It helped us build a deeper understanding of the repo much faster and find research that could make a useful contribution. A few made the cut: ✅ Riemannian Preconditioned LoRA merged - https://github.com/huggingface/peft/pull/3382 🔍 Super-Tuning and Supra in review - https://github.com/huggingface/peft/pull/3518 ⏳ Scaling DoRA implemented and pending license clarification - https://github.com/sockeye44/dorafactors/issues/1 For each one, Outrider pulls together the paper, repo context, reference implementations, benchmarks, and an initial implementation draft. We use that as the starting point to validate the method, work through architecture and API decisions, and iterate with PEFT maintainers. It means we can spend less time searching through papers, codebases, and repo history, and more time on the bigger questions around what is worth contributing and how it should be designed. Try Outrider on your own repo: https://github.com/remyxai/outrider Full contribution report: https://gist.github.com/smellslikeml/dbd577795b7489f6124e2bfbdfc9d28f
repliedto their post 5 days ago
We’ve been using Outrider while contributing to huggingface/peft. It helped us build a deeper understanding of the repo much faster and find research that could make a useful contribution. A few made the cut: ✅ Riemannian Preconditioned LoRA merged - https://github.com/huggingface/peft/pull/3382 🔍 Super-Tuning and Supra in review - https://github.com/huggingface/peft/pull/3518 ⏳ Scaling DoRA implemented and pending license clarification - https://github.com/sockeye44/dorafactors/issues/1 For each one, Outrider pulls together the paper, repo context, reference implementations, benchmarks, and an initial implementation draft. We use that as the starting point to validate the method, work through architecture and API decisions, and iterate with PEFT maintainers. It means we can spend less time searching through papers, codebases, and repo history, and more time on the bigger questions around what is worth contributing and how it should be designed. Try Outrider on your own repo: https://github.com/remyxai/outrider Full contribution report: https://gist.github.com/smellslikeml/dbd577795b7489f6124e2bfbdfc9d28f
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