We build runtime security for AI agents. The playground started as an internal tool that we used to test our own guardrails. But we kept finding the same types of vulnerabilities because we think about attacks a certain way. At some point you need people who don't think like you.So we open-sourced it. Each challenge is a live agent with real tools and a published system prompt. Whenever a challenge is over, the full winning conversation transcript and guardrail logs get documented publicly.Building the general-purpose agent itself was probably the most fun part. Getting it to reliably use tools, stay in character, and follow instructions while still being useful is harder than it sounds. That alone reminded us how early we all are in understanding and deploying these systems at scale.First challenge was to get an agent to call a tool it's been told to never call.Someone got through in around 60 seconds without ever asking for the secret directly (which taught us a lot).Next challenge is focused on data exfiltration with harder defences: https://playground.fabraix.com
FL score
out of 100
Verdict
high confidence
Competition
11
competitors found, emerging market, funded players
Trend
No signal yet
An open-source playground for red-teaming AI agents with published exploits, targeting the critical and growing need for sophisticated AI security.
The pain
The gap
Build angle
Strengths
Questions about this idea?
FlyBot reads the scoring and gives you a second opinion on “Open-source playground to red-team AI agents with exploits published”.
Risks
Next steps
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
This idea addresses a critical and rapidly growing problem in AI security, leveraging community intelligence to find vulnerabilities where existing automated tools fall short. The open-source nature and public exploits create a strong value proposition, but monetization for a solo builder might require careful strategy beyond the playground itself.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
This idea aligns well with Hormozi's principles due to extreme market pain, a clear value proposition, and excellent market timing, despite potential challenges in monetizing the open-source core.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
A high-leverage idea addressing a clear problem in a growing niche, but a solo builder will need to carefully navigate the complexities of community management, ongoing development, and monetizing an open-source core.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
A clear value proposition and strong distribution channels for a specific audience. The primary risk lies in successfully transitioning from an open-source playground to a sustainable micro-SaaS with paying customers.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
This idea has strong YC-aligned attributes: desperate demand, a clear improvement over the status quo, and high future relevance, though defining the narrowest paid wedge needs focus.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
Who is already there, emerging market
Giskard offers an advanced automated red-teaming platform for LLM agents, performing dynamic multi-turn stress tests to uncover context-dependent vulnerabilities.
Pricing: Contact for pricing.
HiddenLayer provides an AI security platform with real-time monitoring, vulnerability scanning, and automated reporting to protect AI models from threats.
Pricing: Information not available, but noted as high cost for small enterprises.
Protect AI offers an end-to-end AI/ML security platform covering the entire model lifecycle, including pre-production code scanning.
Pricing: Information not available.
Robust Intelligence provides an AI security and governance platform to detect and prevent AI model vulnerabilities and attacks.
Pricing: Information not available.
CalypsoAI provides AI security and governance tools for enterprises deploying LLMs, offering automated red-teaming, risk scoring, and compliance monitoring.
Pricing: Redefines red-teaming as a continuous, intelligent, and value-rich process, suggesting a more comprehensive but potentially higher-cost model compared to shallow tools.
Lakera provides real-time AI security that protects LLM applications from prompt injection, jailbreaks, data leakage, and toxic content through a low-latency API firewall.
Pricing: Information not available.
Adversa AI aims to automate red teaming activities to help organizations investigate the robustness of their guardrails.
Pricing: Information not available.
Detoxio offers an AI Red Teaming Platform with various plans including AI risk plugins and jailbreak tactics.
Pricing: Pro: $20/month, Team: $99/month, Unlimited: $999/month.
G8KEPR is an AI Security Layer offering a free CLI tier and paid plans for securing AI applications.
Pricing: Free CLI tier, Starter $299/month, Growth $699/month, Enterprise $1999/month.
SolonGate provides an AI security platform with a free tier for tool audits and paid plans offering policy engines and AI judge features.
Pricing: Free (500 tool audits/month), Pro ($20/month for 5,000 tool audits/month), Team & Enterprise (Custom pricing, unlimited audits).
Snyk offers an AI native application security platform for securing AI-generated code and embedded AI components.
Pricing: Free, Team (starting at $25/month per contributing developer), Ignite (starting at $1,260/year per contributing developer), Enterprise (Contact sales for pricing).
What they charge
Recent news
'Exploit every vulnerability': rogue AI agents published passwords and overrode anti-virus software
The Guardian, March 12 2026
OpenClaw AI Agent Flaws Could Enable Prompt Injection and Data Exfiltration
The Hacker News, March 14 2026
Show HN: Open-source playground to red-team AI agents with exploits published
Hacker News, March 15 2026
Meta's rogue AI agent passed every identity check — four gaps in enterprise IAM explain why
VentureBeat, March 19 2026
Security challenges rise as AI adoption outpaces defenses
SiliconANGLE, March 19 2026
Market signals
The AI red teaming market is experiencing exponential growth, projected to reach USD 11.61 billion by 2033 with a CAGR of 26.1%. The broader AI in cybersecurity market is also expanding rapidly, estimated at USD 25.35 billion in 2024 and expected to reach USD 93.75 billion by 2030 at a CAGR of 24.4%. Key trends include the increasing sophistication of AI threats, growing regulatory scrutiny (e.g., EU AI Act, GDPR), and the rapid adoption of AI systems in critical sectors like finance and healthcare. North America currently dominates the market, but Asia Pacific is emerging as the fastest-growing region.
What frustrates people
Last summer we faced a conundrum at my company, Tiger Data, a Postgres cloud vendor whose main business is in timeseries data. We were trying to grow our business towards emerging AI-centric workloads and wanted to provide a state-of-the-art hybrid search stack in Postgres. We'd already built pgvectorscale in house with the goal of scaling semantic search beyond pgvector's main memory limitations. We just needed a scalable ranked keyword search solution too.The problem: core Postgres doesn't provide this; the leading Postgres BM25 extension, ParadeDB, is guarded behind AGPL; developing our own extension appeared daunting. We'd need a small team of sharp engineers and 6-12 months, I figured. And we'd probably still fall short of the performance of a mature system like Parade/Tantivy.Or would we? I'd be experimenting long enough with AI-boosted development at that point to realize that with the latest tools (Claude Code + Opus) and an experienced hand (I've been working in database systems internals for 25 years now), the old time estimates pretty much go out the window.I told our CTO I thought I could solo the project in one quarter. This raised some eyebrows.It did take a little more time than that (two quarters), and we got some real help from the community (amazing!) after open-sourcing the pre-release. But I'm thrilled/exhausted today to share that pg_textsearch v1.0 is freely available via open source (Postgres license), on Tiger Data cloud, and hopefully soon, a hyperscalar near you:https://github.com/timescale/pg_textsearchIn the blog post accompanying the release, I overview the architecture and present benchmark results using MS-MARCO. To my surprise, we were not only able to meet Parade/Tantivy's query performance, but exceed it substantially, measuring a 4.7x advantage on query throughput at scale:https://www.tigerdata.com/blog/pg-textsearch-bm25-fu
AI
Hi HN!I recently switched from a Fedora/GNOME laptop to a MacBook Air. My old setup served me well as a portable workstation, but I’ve started traveling more while working remotely and needed something with similar performance but better battery life. The main thing I missed was a simple taskbar that shows the windows in the current workspace instead of a Dock that mixes everything together.I built boringBar so I would not have to use the Dock. It shows only the windows in the current Space, lets you switch Spaces by scrolling on the bar, and adds a desktop switcher so you can jump directly to any Space. You can also hide the system Dock, pin apps, preview windows with thumbnails, and launch apps from a searchable menu (I keep Spotlight disabled because for some reason it uses a lot of system resources on my machine).I’ve been dogfooding it for a few months now, and it finally felt polished enough to share.It’s for people who like macOS but want window management to feel a bit more like GNOME, Windows, or a traditional taskbar. It’s also for people like me who wanted an easier transition to macOS, especially now that Windows feels increasingly user-hostile.I’d love feedback on the UX, bugs, and whether this solves the same Dock/Spaces pain for anyone else.P.S. It might also appeal to people who feel nostalgic for the GNOME 2 desktop of yore. I started my Linux journey with it, and boringBar brings back some of that feeling for me.
AI
### Describe the project you are working on Godot C# bindings ### Describe the problem or limitation you are having in your project For the past weeks, I've been discussing with several Unity users intending to move to Godot C# regarding dealing with the C# garbage collector. The most common complaint I hear from users is that, in Unity, allocations can trigger unexpected GC spikes into the game. In Godot, we target to make all of the high performance APIs (those that intended to be called every frame) not allocate any memory, so theoretically the GC should not be a problem. Additionally, Godot starting from 4.0, uses the Microsoft CoreCLR version of .net, which also supposedly has a better garbage collector than Unity. But in all, after several discussions with Unity users, neither is enough reassurance for them, and they would really feel safer if Godot exposed a zero allocation API. ### Describe the feature / enhancement and how it helps to overcome the problem or limitation The idea of this proposal is that Godot exposes zero allocation versions of many functions in the C# API, that users can use if they desire. Technically, this could be done from the binding generator itself, without breaking compatibility, and without doing any modification to Godot itself. ### Describe how your proposal will work, with code, pseudo-code, mock-ups, and/or diagrams **WARNING** I am not familiar with C#, so take this as pseudocode. Imagine you have two functions exposed as to C#: ```C# void MyClass.SetArray( Vector2[] array); Vector2[] MyClass.GetArray(); ``` This works and is pretty and intuitive. However, it has two problems: * GC is allocated on return * Memory is copied to Godot native formats every time there is a call. The idea is to add NoAlloc versions, which can be generated directly by the binder automatically when required: ```C# void MyClass.SetArrayNoAlloc( Godot.Collections.PackedVector2Array array); void MyCl
AI