Inspiration
AI agents are becoming more capable every day, but that also means they're exposed to an entirely new set of security problems. Most security tools today were built for web applications, APIs, and infrastructure. They were never designed to handle prompt injection, jailbreaks, system prompt leaks, tool abuse, or agent manipulation, so they often miss these issues completely.
What really inspired us was seeing how easy it has become to build an AI agent compared to how difficult it is to properly secure one. Anyone can launch an AI application in a matter of minutes, but very few developers have the time or security expertise to thoroughly test it before putting it into production. We wanted to make AI security testing something every developer could use instead of something reserved for companies with dedicated security teams.
What it does
Agentic Security is an autonomous red teaming platform built specifically for AI applications and LLM agents. Instead of running a one-time scan or replaying a list of known attack prompts, it actively generates new attacks based on how your application behaves. As it learns more about the target, it adapts its testing strategy to uncover deeper vulnerabilities.
The platform tests for prompt injection, jailbreaks, system prompt leakage, tool abuse, agent manipulation, multimodal attacks, and other AI-specific security issues. It also performs AI fuzzing, mutation testing, adaptive attack planning, and automatically correlates findings into a report that developers can actually use.
The biggest difference between our platform and many existing solutions is that we are not relying on a static database of prompts. Our autonomous agents continuously generate new attack strategies during testing, which allows them to discover failure modes that may never have been documented before.
How we built it
We built Agentic Security as a collection of specialized components that work together throughout the testing process.
The Attack Generation Engine is responsible for creating adversarial prompts and exploit chains. Rather than following a fixed script, it observes how the target responds and generates attacks that are tailored to that behavior.
The Jailbreak Simulation Framework focuses on testing known and emerging jailbreak techniques, including roleplay attacks, instruction bypasses, context manipulation, and prompt obfuscation.
Our AI Fuzzing Engine mutates prompts, conversation history, and contextual information to uncover edge cases and unexpected behaviors that traditional testing would likely miss.
We also built multimodal security testing so the platform can evaluate attacks that involve images as well as text. This includes hidden instructions inside images, OCR-based attacks, and other cross-modal injection techniques.
To make testing improve over time, we added a reinforcement learning layer. Whenever an attack successfully exposes a weakness, the system builds on that result by refining similar attack strategies for future testing sessions.
One feature we are especially excited about is our MCP server integration. Instead of learning a separate interface, developers can simply ask an AI assistant something like "Scan my chatbot for prompt injection vulnerabilities." Agentic Security performs the assessment in the background and returns the findings directly in the conversation.
Challenges we ran into
One of the hardest parts of the project was dealing with the fact that LLMs are not deterministic. Traditional software usually behaves the same way every time, making bugs relatively easy to reproduce. AI systems are different. A vulnerability might only appear after a specific sequence of prompts or under a particular conversational context, which makes reliable testing much more difficult.
Another challenge was balancing quantity with quality. Generating thousands of random attacks is easy, but most of them provide very little value. We spent a lot of time making sure our autonomous agents focused on attacks that revealed meaningful and actionable security issues instead of simply producing more output.
We also invested a significant amount of time designing adaptive attack planning, vulnerability scoring, and reporting that would be useful to developers instead of overwhelming them with raw data.
Accomplishments that we're proud of
We built a security platform designed specifically for AI-native threats instead of adapting traditional cybersecurity tools. We successfully implemented autonomous attack generation, combined jailbreak testing, fuzzing, multimodal analysis, and exploit planning into a single platform, and integrated MCP so developers can launch security assessments using natural language. We also designed the architecture to be flexible enough to support future AI security research and new attack techniques as the ecosystem evolves.
What we learned
The biggest lesson we learned is that securing AI systems requires a completely different mindset than securing traditional software. Many of the most serious vulnerabilities are not bugs in the conventional sense. They emerge from prompts, memory, context, tool usage, and the way autonomous agents make decisions.
We also learned that autonomous agents can be just as valuable for defense as they are for building applications. Instead of manually thinking of every possible attack, autonomous systems can continuously generate new ideas, learn from successful exploits, and become more effective over time.
What's next for Agentic Security
Our goal is to turn Agentic Security into a complete AI security platform that continuously monitors, tests, and improves AI systems throughout their entire lifecycle. We plan to expand into multi-agent attack simulations, enterprise compliance reporting, real-time monitoring, broader multimodal threat detection, automated remediation suggestions, and security benchmarking across major LLM providers.
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