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Ship AI features without shipping vulnerabilities. PromptBrake is a pre-release security testing platform for LLM-powered APIs โ point it at your endpoint and run repeatable scans for prompt injection, data leaks, unsafe tool use, and output bypasses, mapped to the OWASP LLM Top 10.
Each finding includes evidence logs and remediation guidance, so you know exactly what to fix. Compare runs to catch regressions, and plug the same checks into CI to gate risky releases.
Works with OpenAI, Claude, Gemini, and custom LLM-backed endpoints. Also includes free tools such as an LLM security checklist, a prompt injection payload generator, and an OWASP LLM test case mapper to help teams validate their setup early.
PromptBrake GitHubPromptBrake's answer
Choose PromptBrake when you want a security check, not a security project. Open-source tools like Promptfoo and Garak are powerful, but expect you to design your own test suite, write probes, and interpret raw output โ that's a week of setup before your first useful result. Manual AI pentests deliver depth but cost $5,000โ$20,000 per audit and run at best quarterly. PromptBrake fits the gap: an opinionated, repeatable suite you can run before every release, with results your engineers can act on without a security background. If your team ships AI features faster than a security calendar can keep up, that's the workflow PromptBrake is built for.
PromptBrake's answer
Backend: Python with Flask, SQLAlchemy, Flask-Migrate (Alembic), and Gunicorn. Database: SQLite. Auth and identity: Firebase Authentication. Payments: Stripe. Frontend: server-rendered Jinja2 templates with vanilla JavaScript and CSS โ no SPA framework. Infrastructure: AWS (EC2), with Cloudflare in front for WAF and CDN.
PromptBrake's answer
PromptBrake is the only LLM API security scanner built as a product, not a framework. While open-source tools like Promptfoo and Garak give you a flexible harness for building your own red-team workflow, PromptBrake gives you an opinionated 12-test suite (60+ real-world attack scenarios, OWASP LLM Top 10-aligned) that runs against your endpoint with zero configuration. Every finding comes with PASS/WARN/FAIL verdicts, evidence logs, and concrete remediation steps โ so engineers know exactly what broke and how to fix it. Compare any two runs to track regressions and fixes between releases, and drop the same scans into CI as a release gate. Works with OpenAI, Claude, Gemini, and custom LLM-backed endpoints. No security team, no harness setup, no agent install โ point it at your endpoint and ship with confidence.
PromptBrake's answer
Engineering teams shipping LLM-powered APIs who are accountable for security but don't have a dedicated security team. That includes startup engineers wiring an AI feature into production, platform teams adding LLM endpoints to existing apps, and solo builders who need a credible security check before launch. The common thread: they own an AI endpoint, they're shipping fast, and they need a repeatable answer to "is this safe to release?" without hiring a pentester or building a red-team harness from scratch.
PromptBrake's answer
If you've shipped an LLM-powered API, you've probably had this moment: someone asks, "Is it secure?" and you don't know where to start. OWASP publishes a Top 10 for LLMs. Headlines warn about prompt injection, jailbreaks, data leaks, and tool abuse. Blog posts disagree on what to test and how to test it. Open-source frameworks hand you a toolkit and assume you already know what to do with it. Manual pentests cost probably $5,000 to $20,000 and take weeks to schedule. Most engineers shipping AI features aren't security researchers โ they need a credible answer before the next release goes out. That's the gap PromptBrake was built for. One opinionated suite. Twelve tests covering what actually matters. PASS/WARN/FAIL results with evidence and remediation in plain language. Point it at your endpoint, see what's broken, fix it, ship. Security testing shouldn't require becoming a security expert first.
Based on our record, GitHub seems to be more popular. It has been mentiond 2463 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
The core of the ecosystem is the official open-source server hosted on GitHub. It is written in TypeScript and implements the full MCP specification. - Source: dev.to / about 4 hours ago
This is why the gate needs a trace it can trust, and why AgentLens is the other half of this workflow. agent-eval scores and gates the output; AgentLens captures the trace of how the agent got there โ every model call and tool step, the resolved inputs (not the templated ones), the raw outputs. That trace is exactly the unforgeable, agent-didn't-author substrate that Tier 1+2 need to score against. Without it,... - Source: dev.to / about 16 hours ago
## Tell Git to start tracking your project Git init ## Take a snapshot of all your current files Git add . ## Save this snapshot with a description Git commit -m "Initial commit from AI tool" ## Connect your local project to GitHub ## Get repository URL from your GitHub page ## it looks like https://github.com/your-name/your-repo.git Git remote add origin PASTE_YOUR_URL_HERE ## Upload your code to GitHub Git... - Source: dev.to / 10 days ago
Conclusion Next time Git insists a private repository doesn't exist, skip editing your config file and head straight to the Windows Credential Manager. Wiping out the stale git:https://github.com entry forces a clean handshake, getting you back to coding in less than a minute. - Source: dev.to / 11 days ago
Gitea is where all private repositories live: infra configs, personal projects, anything I don't want on a third-party server. Public projects still go to GitHub because that's where the audience is, but a number of those GitHub repositories are mirrored back to Gitea as a local backup. The split is simple: Gitea for control and resilience, GitHub for reach. - Source: dev.to / 12 days ago
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