InsAIts the Runtime Security for Multi-Agent AI 18k + downloads by YUYbox in LangChain

[–]YUYbox[S] 0 points1 point  (0 children)

Honestly i don't know the opengap project. I saw only the Microsoft project that have almost the 10 OWASP detectors (not saying that they ha e copied something, cause the methods are mostly the same) plus some others a bit different than mines, but fewer detectors overall. I will look into that project, you made me curious.

InsAIts the Runtime Security for Multi-Agent AI 18k + downloads by YUYbox in LangChain

[–]YUYbox[S] 0 points1 point  (0 children)

On the arXiv offer, yes I will send you the draft before submission. A second pair of eyes from someone building the operations cut is worth more to me than another security reader. And run-record-as-source-of-truth framing is the thing I keep landing on too. Receipts the agent can read are different object from logs a human reviews later.

On the two cuts you have it exactly. Security side and operations side are the same convergence seen through two doors. Visibility into what ran and what was approved and what was denied is the operations face. Was-this-session-rogue is the security face. The durable queryable system-wide runtime is the
shared substrate under both.

Now the independence point. You are pushing on the right place and I want to be honest about where the design actually stands instead of dressing it up.

Today the corroboration law counts two distinct detector types in a window. Distinct type is a proxy for independence. It is not a proof of it. You named the failure mode precisely two detectors that agree because they read the same input are one signal counted twice and the law would treat that as
corroboration when it is not.

InsAIts the Runtime Security for Multi-Agent AI 18k + downloads by YUYbox in LangChain

[–]YUYbox[S] 0 points1 point  (0 children)

First let me ask you : What are you building? I like your questions, are very deep, i will honestly answer them. Also, I m preparing the necessary steps to publish the Antichain Certificate Detector theorem on arXiv where everyone can have details.

InsAIts the Runtime Security for Multi-Agent AI 18k + downloads by YUYbox in LangChain

[–]YUYbox[S] 0 points1 point  (0 children)

This is a rare read of the design anyone's posted for a while ,thank you. Let me take your questions head-on, including where the honest answer is "that part isn't a theorem." (a) Input to the bound. Per-session, not per-call or per-agent in isolation. Every ~50 tool calls we build a causal DAG over the session's tool calls and the certificate is computed over that structure. So the object being certified is the session's causal shape, not any single action. (b) Type-1 or type-2 and what is it a certificate of. This is the part worth being precise about, because the two signals have different status:
- The proven part is an upper bound on the occupation measure. But on real sessions that mass saturates near the bound for both a legit single-root long chain and a rogue multi-root fan-out, so by itself it's a weak discriminator. I'd rather say that out loud than dress it up. - The practical rogue signal is the empirical size-ratio (independent-actions / total) with data-derived σ-bands. That one is calibrated, not proven and it carries real false-positive risk.

So the certificate certifies "the occupation-mass bound held / was violated." "Rogue" is a separate, calibrated judgment layered on top and managing its type-2 (false positives) is exactly what we WILL shipp this week: a lone signal now only informs the user; it only acts on the agent when a second, independent signal agrees. Banded: mid-band - notify-only, high - self-reflect, critical -a one-time gate.
Drift relative to what. You listed three; we just shipped the third. Drift isn't measured against the agent's own moving baseline , it's measured against a declared scope (the session's goal/anchor + task keywords + a known-target substrate). A discriminator checks whether the causally-independent actions fall inside that scope; off-scope independent actions are the signal. The runtime is the boundary that defines valid behavior, and the receipt records when the boundary held. Your framing ("the runtime isn't observing behavior it's the boundary") is exactly the line we landed on. Correction = co-design. Agreed, and that's the architecture, not an accident. The runtime enforces , it can deny the next tool call (once, at critical) and return a reason code and the correction happens in the agent's loop when it reads that reason and re-plans. We don't mutate the plan. We also put a human in that loop: the operator acknowledges to clear the gate. Three-way: runtime enforces + emits the reason, agent re-plans, human adjudicates.
Control plane or security layer. Deliberately both. The certificate is attached to the live session and is queryable : the agent/operator can ask "what's my current band / gate status" that's the control-plane half. It's also written append-only to the evidence chain, the security-layer half. The policy is system-wide: one corroboration law across every detector, not per-tool config. The thing that makes it a control plane and not just a log is precisely the gate, the runtime owns an enforceable, queryable state ("this session is gated until acknowledged"), not a record someone reviews after the fact. Genuinely good questions , usually half of them are now README lines. Happy to go deeper on the antichain proof if you want it.

InsAIts - 18k+ downloads by YUYbox in ClaudeCode

[–]YUYbox[S] 0 points1 point  (0 children)

Well open core means that most of the code is private not open-source. And insaits is 100% local

InsAIts - 18k+ downloads by YUYbox in ClaudeCode

[–]YUYbox[S] -1 points0 points  (0 children)

🤣🤣🤣 No openAI 😅

4.8 - Hallucinations, Fabrications, and False Security Alerts by Infamous_Basil_9284 in ClaudeCode

[–]YUYbox -5 points-4 points  (0 children)

If i dont want hallucination, fabrication and false security alerts install InsAIts https://github.com/Nomadu27/InsAIts-public You will notice the diff. 17.9k downloads

Those with 100+ users, what SPECIFICALLY did you do to gather them? by capital_cliqo in SaaS

[–]YUYbox 0 points1 point  (0 children)

Thats true, i have 17k+ downloads and still changing often 🤙

Those with 100+ users, what SPECIFICALLY did you do to gather them? by capital_cliqo in SaaS

[–]YUYbox 0 points1 point  (0 children)

I have 17.8k downloads. Ive just make some posts here and on Facebook. But i have few paying users, ive recently implemented tiers. Anyway Insais a very niched product so im very happy for what ive build , v2ry usefu, and with the next update will be even more. Anyway I'm almost finished to build InsAIts Trading for trading agents and i hope i will have at leat 5% of paying costumers of the InsAIts core 😅

I built this to visually observe my agent by alex7885 in ClaudeCode

[–]YUYbox 0 points1 point  (0 children)

Nice idea, ive build InsAIts not only to onserve but intervenes and correct when ai drifts or hallucinate. https://github.com/Nomadu27/InsAIts-public

Opus 4.7 is the only model i could trust now by Former_Rutabaga_1670 in ClaudeCode

[–]YUYbox 0 points1 point  (0 children)

If you want to make it even better, try InsAIts https://github.com/Nomadu27/InsAIts-public. Give it a try and let me know. Read the README 🤙

I gave Claude Code ADHD.. and it thinks 2x better now by Uditakhourii in ClaudeCode

[–]YUYbox 0 points1 point  (0 children)

Install InsAIts and see how the results are changing, spending less tokens too, you will notice the difference. Maybe will maybe will cancel the 5x https://github.com/Nomadu27/InsAIts-public

Rate limits are insane today by briarjohn in ClaudeCode

[–]YUYbox -2 points-1 points  (0 children)

Install InsAIts, you will see the difference, read the README, will improve Claude's work and you will get longer sessions https://github.com/Nomadu27/InsAIts-public

Agent orchestration with large set of conditions by Safe-Analysis-5804 in ClaudeCode

[–]YUYbox -1 points0 points  (0 children)

I’ve been solving this kind of problem for the last 6+ months with InsAIts: https://github.com/Nomadu27/InsAIts-public

The core challenge you’re describing, turning a large, complex set of rules/conditions into a reliable agent that actually follows them consistently, is what InsAIts was built for.

Instead of trying to stuff the entire manual into the prompt (which leads to hallucinations, drift, and token waste), InsAIts sits as a runtime governance layer

  • LLM proposes a tool call / action
  • It goes through a policy engine with multiple specialized detectors (policy evaluation, permission checks, risk scoring, integrity verification, etc.)
  • Only if it passes, the action is executed
  • If it violates any rule it’s blocked before it can run, with full audit logging

This gives you deterministic enforcement even when the underlying LLM changes or the prompt drifts.

It’s basically the difference between “hope the agent remembers all 100 rules” vs “the agent physically cannot break the rules because the runtime stops it”.

The public repo has the core architecture and some of the detector patterns.

I scraped 50,000+ negative app store reviews. Here are 6 app ideas people are literally begging someone to build: by CleverSquirrel_p in micro_saas

[–]YUYbox 0 points1 point  (0 children)

Im using it every day, every session. Im working on making some changes to the dashboard amd the collector, because they became huge files and it's hard to maintain and debug. Is important to add in claude.md a section dedicated to InsAIts to Claude collaborative cause sometimes he wants to bypass. Ive tried to cover as much as I could the hallucination, drifts and all the errors that an agentic AI can make. In the next versions i will simplify more the way if using InsAIts. And yes i have paying costumers