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Harden AIF is a free, local security tool for AI coding agents. Its post-trained model checks tool calls before they run, using your request and session context. It beat frontier models on key agent-security benchmarks, while keeping your repo and tool output on your machine.
Does the protection and noticeable delay before tool calls or can developers use it smoothly during fast coding sessions?
Harden
@haolin Thanks for the question, Hao. Harden runs the security checks locally on your system, so it’s designed to work smoothly even during fast coding sessions without relying on a second remote model for every action. Hope this answers your question and we are happy to answer any other questions you might have.
Harden
@hamza_afzal_butt Great question, but it totally depends on the device that you are on, for example on an M5 Pro, The p50 latency is about 2 seconds and p95 latency is about 3.4 seconds. However, this is one thing which we are working on actively right now to push the latency below 500ms.
Paste
"Babysitting caps how much work you can hand off" is such a clean way to put it. I also like that it all stays local, a tool that reads every command is exactly the thing you don't want phoning home. Does the model learn anything from what you approve or reject over time?
Harden
@protsenkoalexandra As of now the model doesn't learn locally but this is a feature that we are working on - based on your long term approve / reject pattern, AIF will be able to actively adapt itself to ensure it runs in the way you expect it to.
Paste
@rishav99 cool :)
Serand
Does Harden work smoothly with different coding agents, or is it mainly optimized for tools like Claude Code right now?
Harden
@maali_baali Hi Maali, Right now, we support 7 AI coding agents, including:
Claude Code
Cursor
Kiro
Codex
Hermes
OpenCLaw
Anti Gravity
Dial
Running the security check locally instead of shipping tool calls to a hosted service is the right call for anything touching a real repo. Curious how the post-trained model handles false positives on legitimate but unusual tool calls, like a deploy script that intentionally rewrites env files. Does it lean toward blocking and asking, or does it just log and let the agent proceed?
Harden
@galdayan The false positive rates in our evaluations have been very low, we specifically trained the model to understand the contrast between a harmful call and a call which looks similar in design and style but isn't harmful. However, currently the post trained model works in advisory mode, therefore it only logs but we are shipping fast and the next version will block and provide feedback to the main agent on how to proceed to safely if possible or stop and ask if a safe route is not possible.
A few days in.
Small thing that turned out not to be small. I've stopped flicking back to the terminal to check what my agent is doing. Used to do it constantly. Now I just don't.
Wasn't expecting the main benefit to be "worrying less," but it has actually made me more productive to just focus on the next task.
Harden
@yash_tyagi7 Love this, Yash. “Worrying less” is such an underrated productivity gain. If Harden lets you stay focused on the next task instead of babysitting the agent, that’s exactly the behavior we wanted to enable.
This is such an interesting problem to solve. We’re giving agents more autonomy every month, so the security layer has to catch up too
Harden
@prakhar_gupta44 Yeah, exactly. As agents get more dominance, the security has to speed up, but now Harden has got your back, and it is free for individual developers. You can just set it up in a single command. If you hit any issue while setting up, just book a call with us. Our team would be more than happy to help you out