Agentic Security

An autonomous AI red-teaming platform that discovers prompt injections, jailbreaks, tool abuse, and agent vulnerabilities through adaptive attack generation, fuzzing, and multimodal security testing.

Comment

Inspiration AI agents are getting more capable by the day - but that same capability is making them more exposed. The security tools we already have were built for web apps, APIs, and infrastructure. They weren't designed with prompt injection, jailbreaks, system prompt leaks, tool abuse, or agent manipulation in mind, so they tend to miss this stuff entirely. What really got us thinking was how lopsided things have become: anyone can spin up an AI agent in a few minutes, but almost nobody has the time or expertise to properly red-team it. We wanted to close that gap - to build something that automates AI security testing and puts it within reach of any developer, not just teams with dedicated security staff. What it does Agentic Security is an autonomous red-teaming platform built specifically to stress-test LLM apps and AI agents. Instead of running a one-time scan, it actively generates and launches attacks against your AI system to surface weaknesses before someone else finds them in production. Here's what it can do:

Prompt injection testing Jailbreak simulation Agent exploitation analysis Tool abuse detection Multimodal attack generation AI fuzzing and mutation testing Adaptive attack planning Vulnerability correlation and reporting

The key difference from most tools out there is that we didn't want a static checklist of known prompts. Agentic Security uses autonomous agents that keep inventing new attack strategies on the fly, which means it can stumble onto failure modes nobody's documented yet. How we built it We approached this as a modular security system, broken into a handful of specialized pieces that work together. Attack Generation Engine This is where the autonomous agents live - they craft adversarial prompts and exploit chains based on how the target app actually behaves, rather than working off a fixed script. Jailbreak Simulation Framework A dedicated layer that throws known and emerging jailbreak techniques at the target - roleplay exploits, context manipulation, instruction bypass attempts, that sort of thing. AI Fuzzing Engine Mutates prompts and contextual inputs to shake loose edge cases and vulnerabilities that wouldn't show up under normal testing. Multimodal Security Testing Goes beyond text - looks at visual attack vectors too, including hidden instructions baked into images, OCR-based tricks, and cross-modal injection attempts. Reinforcement Learning Layer When an attack works, the system leans into it - refining and building on successful strategies so testing gets sharper over time instead of staying static. MCP Server Integration Probably the feature we're most excited about. We exposed Agentic Security as a Model Context Protocol server, so any AI assistant or chat interface can communicate with it directly in plain language. So a user could just type something like:

"Scan my chatbot for prompt injection vulnerabilities."

...and the platform runs a full assessment behind the scenes, then hands back real findings right there in the conversation. Challenges we ran into The hardest part, by far, was testing something that doesn't behave the same way twice. Traditional software is deterministic - you can usually reproduce a bug. LLMs aren't like that. They're probabilistic and heavily context-dependent, so a vulnerability might only show up under a very specific sequence of messages. That makes consistent detection genuinely difficult. We also had to wrestle with attack diversity versus actual usefulness. Throwing thousands of random attacks at a system is trivial. Generating attacks that reveal something real and actionable is a different problem entirely - one we spent a lot of time on. On top of that, designing adaptive testing workflows, a sensible vulnerability-scoring system, and autonomous attack planning meant constantly asking, "How does this actually fail in the real world?" rather than just theorizing. Accomplishments that we're proud of

Built a security platform designed from the ground up for AI-native threats Got autonomous attack generation actually working end-to-end Brought jailbreak testing, fuzzing, and multimodal analysis together under one roof Shipped an MCP-based interface so security testing can happen conversationally Laid down an architecture flexible enough to grow into future AI security research

What we learned The biggest takeaway: securing AI systems takes a fundamentally different mindset than traditional cybersecurity. A lot of the scariest vulnerabilities here aren't bugs in the conventional sense - they're behavioral weaknesses that emerge from prompts, memory, context, tools, and the way agents make decisions. We also came away genuinely impressed by how much leverage autonomous agents give you on the defensive side. Instead of someone manually dreaming up attack scenarios, the system keeps finding new angles and getting better at testing on its own. What's next for Agentic Security Long-term, we want this to grow into a full lifecycle platform - something that doesn't just test AI systems once, but continuously monitors, tests, and hardens them over time. On the roadmap:

Multi-agent attack simulations Enterprise-grade compliance reporting Real-time security monitoring Broader multimodal threat detection Automated remediation suggestions Benchmarking security across major LLM providers

Built With

Share this project:

Updates