Agentic Security: LLM Vulnerability Scanner Aggregator

Inspiration

While building a real-world RAG system with agentic workflows, I realized I had no reliable way to attack my own LLMs like an actual adversary would.[web:105] Existing tools felt too narrow (only jailbreaks or only injections), too static (fixed prompt lists), or disconnected from modern agent setups.[web:105] I wanted something that would actively probe models across modalities, adapt its strategy, and speak the same “chat” language developers already use.[web:107]

What it does

Agentic Security is an agentic LLM vulnerability scanner aggregator.[web:105] It orchestrates multiple attack strategies to pressure‑test LLMs:

Probes jailbreaks, prompt injections, and adversarial prompts across text (and is designed for images/audio next).[web:105]
Simulates multi‑step, agent‑style attack chains instead of just single prompts.[web:107]
Runs fuzzing with randomized edge‑case inputs to surface unexpected failures.[web:105]
Uses reinforcement‑style feedback to prioritize attacks that are more likely to break a given model.[web:105]
Produces concise vulnerability reports with early‑stopping and token‑aware optimization so tests stay practical.[web:105]

All of this is wrapped in a chat‑driven flow, so you can trigger deep security testing from a single command instead of juggling multiple tools.[web:107]

How we built it

I started with a modular architecture:[web:105]

An attack generator layer for jailbreak templates, injection patterns, and fuzzing strategies.
An execution engine that runs attacks against target LLMs and captures responses.
An adaptive “attack director” that learns which probes are working and refines future ones.
A reporting module that aggregates results into risk scores, examples, and recommended mitigations.[web:105]

On top of this core, I’m wiring it into a Model Context Protocol (MCP) server, so it can plug into LLM chat UIs and dev workflows.[web:107] From there, a developer can say “scan this model” and have Agentic Security handle the orchestration, logging, and summaries automatically.[web:107]

Challenges we ran into

Depth vs. efficiency: fuzzing and adaptive attacks can explode in cost and time, so I designed early‑stopping rules, sampling strategies, and token budgets.[web:105]
Multimodal alignment: designing one pipeline that can handle text today and image/audio attacks tomorrow required careful abstraction around payloads and results.[web:105]
Developer UX: security tools are usually CLIs and dashboards; making this feel natural as a chat‑triggered agent meant rethinking how to present findings and controls in conversation form.[web:109]

Accomplishments that we're proud of

Built an all‑in‑one LLM security suite that unifies jailbreaks, prompt injections, fuzzing, and adaptive attacks behind a single interface.[web:105]
Turned complex research‑grade attack techniques into a simple, chat‑first workflow developers can use during builds.[web:107]
Implemented smart reporting that highlights concrete failures instead of dumping raw logs, while staying token‑efficient.[web:105]
Designed the system to be open and extensible so individuals and teams can plug in new attack patterns or scanners as threats evolve.[web:109]

What we learned

Working on Agentic Security reinforced that LLM security is a moving target—the attacks change as quickly as the models do.[web:105] Reinforcement‑style adaptation can uncover failure modes that static prompt lists would never hit.[web:105] I also learned how important it is to meet developers where they are: inside chats and agent workflows, not just in separate security tools.[web:107] Bridging those worlds makes it far more likely that security testing actually happens, not just gets planned.[web:105]

What's next for Agentic Security: LLM Vulnerability Scanner Aggregator

Expand the MCP server integration so Agentic Security can sit behind more LLM platforms and IDEs.[web:107]
Build a community‑driven library of attack recipes to keep pace with new jailbreaks and prompt‑injection tricks.[web:105]
Optimize for team and enterprise use: configurable policies, scheduled scans, and CI/CD hooks.[web:109]
Push toward live, in‑the‑loop monitoring so models can be watched and probed continuously, not just at deployment time.[web:105]

Agentic Security is meant to grow alongside LLMs themselves—an evolving agent that keeps your agents honest.[web:105]