Open-source playground to red-team AI agents with exploits published
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
- Addresses a severe and growing problem in AI security.
- Open-source and community-driven approach provides a unique, leveraged advantage over traditional solutions.
- Published exploits and logs offer transparency and learning, solving the 'shallow' problem of existing tools.
- Strong demand and willingness to pay for effective AI security solutions.
- Market is experiencing exponential growth and increasing regulatory scrutiny.
Risks
- High competition from multiple well-funded incumbents.
- Monetizing an open-source core effectively without alienating the community.
- Maintaining and scaling the complex technical infrastructure and community engagement as a solo builder.
- Requires deep expertise in AI security beyond just building the playground to offer premium services.
- Converting free playground users into paying customers can be challenging.
Next steps
- Conduct interviews with security engineers and AI architects in enterprises to validate specific pain points and willingness to pay for premium features (e.g., private challenges, advanced analytics, custom guardrails).
- Develop a clear roadmap for premium offerings based on validated demand, focusing on a minimal viable paid product.
- Engage actively with the existing open-source community to understand their needs and identify potential contributors or early adopters for paid features.
- Explore partnerships with AI security consultants or agencies to accelerate go-to-market for enterprise solutions.
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 26
- Solution gap
- 20
- Willingness to pay
- 20
- Buildability
- 12
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
- Conduct interviews with security engineers and AI architects in enterprises to validate specific pain points and willingness to pay for premium features (e.g., private challenges, advanced analytics, custom guardrails).
- Develop a clear roadmap for premium offerings based on validated demand, focusing on a minimal viable paid product.
- Engage actively with the existing open-source community to understand their needs and identify potential contributors or early adopters for paid features.
- Explore partnerships with AI security consultants or agencies to accelerate go-to-market for enterprise solutions.
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 (AGISec Technologies)Visit
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.
- G8KEPRVisit
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.
- SolonGateVisit
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
- Traditional manual penetration tests for AI are expensive.
- Automated AI red teaming tools are often shallow and rely on outdated attacks or generic prompts that adversaries can bypass.
- Reactive fixes to credential exfiltration vectors (e.g., rotating keys, patching prompts) are not durable; a more robust approach is limiting what credentials can do.
- Integrating AI into work can lead to glaring errors, sloppy code, and reduced productivity.
- Traditional security controls lack visibility into what happens inside live AI agent sessions after access is granted.
Semble – Code search for agents that uses 98% fewer tokens than grep
Hey HN! We (Stephan and Thomas) recently open-sourced Semble. We kept running into the same problem while using Claude Code on large codebases: when the agent can't find something directly, it falls back to grep, reading full files or launching subagents. This uses a lot of tokens, and often still misses the relevant code. There are existing tools for this, but they were either too slow to index on demand, needed API keys, or had poor retrieval quality.Semble is our solution for this. It combines static Model2Vec embeddings (using our latest static model: potion-code-16M) with BM25, fused via RRF and reranked with code-aware signals. Everything runs on CPU since there's no transformers involved. On our benchmark of ~1250 query/document pairs across 63 repos and 19 languages, it uses 98% fewer tokens than grep+read and reaches 99% of the retrieval quality of a 137M-parameter code-trained transformer, while being ~200x faster.Main features:- Token-efficient: 98% fewer tokens than grep+read- Fast: ~250ms to index a typical repo on our benchmark, ~1.5ms per query on CPU (very large repos may take longer)- Accurate: 0.854 NDCG@10, 99% of the best transformer setup we tested- MCP server: drop-in for Claude Code, Cursor, Codex, OpenCode- Zero config: no API keys, no GPU, no external servicesInstall in Claude Code with: claude mcp add semble -s user -- uvx --from "semble[mcp]" sembleOr check our README for other installation instructions, benchmarks, and methodology:Semble: https://github.com/MinishLab/sembleBenchmarks: https://github.com/MinishLab/semble/tree/main/benchmarksModel: https://huggingface.co/minishlab/potion-code-16MLet us know if you have any feedback or questions!
AI
Postgres extension for BM25 relevance-ranked full-text search
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
Consus (YC F24) – LLM gateway for companies handling sensitive data
Hey Hacker News! I'm Eric, the founder of Consus. We route all AI requests to ITAR restricted and controlled unclassified information (CUI) compliant infrastructure. Consus itself satisfies ITAR restrictions, and we recently completed our FedRAMP equivalency audit against the 410 FedRAMP High controls. FedRAMP High is the U.S. government’s strictest security baseline for cloud services handling sensitive unclassified data. A breach of this data could cause severe or catastrophic harm. Achieving FedRAMP equivalency, or authorization, is required before processing CUI information.Demo: https://youtu.be/6MYxJe6P4oYAerospace and defense is experiencing rapid growth. Unfortunately, most companies are struggling to use AI because of necessary data protections & compliance risk. These companies can move even faster if they could use the AI tools that most other industries take for granted.I spent most of my career prior to Consus in aerospace & defense. I launched national security payloads at SpaceX, and was an early employee at Castelion. I’ve watched first hand how compliance red-tape makes it difficult for companies to adopt new technologies and trust vendors that touch their sensitive data. For example, I worked with a satellite manufacturer that doesn’t allow any AI tools, even for non-sensitive work.Even as AI has become extremely useful over the past 9-12 months, I was surprised by how many aerospace & defense companies refused to touch it. They could see how it could be helpful, but they were scared about model providers training on their data, violating an ITAR rule, or risk losing new business because of a failed cyber assessment. These are all rational fears.There were a lot of ways to solve some/all of these problems, but we focused on building a compliant AI inference layer that can "plug" into any AI tool or workflow. Just as OpenRouter integrates with most tools, Consus can connect to tools like ChatGPT or Clau
AI
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