• 

  cyberhuntlab

Got it, Carol. Let's strip away the commercial talk and focus 100% on the core technical architecture and the pure engineering behind the project. Here is the clean, official English version of your project's history without any emojis, store references, or business models, written with the technical depth that hackathon judges want to see:

Cyber Hunter Lab: Automated AI-Driven Logic & Vulnerability Audit Pipeline

Inspiration

The vast majority of traditional security tools focus on static analysis or known signatures. They fail completely when trying to identify Business Logic Vulnerabilities, such as mathematical billing manipulation or input integrity bypasses. Currently, researchers and Bug Hunters waste hours writing manual reports and trying to guess target structures on GitHub. Our inspiration came from a clear technical insight: business logic flaws and infrastructure responses are universal, governed by bit behavior and deterministic mathematics. We decided to create an automated pipeline capable of mapping these flaws dynamically, transforming an isolated testing workflow into a portable, high-efficiency automation environment.

How we built it

The core engine of Cyber Hunter Lab was built using Python, fully optimized to run natively and efficiently in mobile environments by communicating directly with Linux Kernel processes through Android architectures. The system operates as a secure Black-Box infrastructure based on a stateful mutation loop. To model the financial impact of a business logic bypass (CWE-697), we implemented a billing signal inversion algorithm. The dynamic risk exposure per transaction (E_R) is calculated at the bit level using the following function: Where:

• V_{\text{base}} is the configured base transaction value at the endpoint.
• x is the mutated numerical input vector injected into the entry channel (x < 0).
• \Delta_m is the continuous mutation scaling factor driven by the local state machine (hunter_state.json). The engine processes packets, validates arithmetic errors (CWE-369) through physical boundary checks \lim_{d \to 0} \frac{N}{d}, and exports a standardized universal JSON schema. This clean output is ingested by AI models, such as the Qwen Cloud, which translate raw compliance data into executive-level reports ready for platforms like HackerOne and Bugcrowd. ## Challenges we overcame
• Isolation and System Architecture: Ensuring a powerful auditing tool could run flawlessly inside a secure mobile sandbox environment without exposing the internal codebase or requiring access to the target's private code. We solved this by structuring the architecture strictly around input-output behavioral testing boundaries.
• The Human Factor in Triaging: Preventing automated reports from being discarded by junior triagers. We overcame this challenge by designing an output pipeline that converts complex JSON data into a clear, step-by-step manual replication sequence that allows triagers to instantly verify the logical flaws. ## What we learned We learned that mathematics does not lie. It is not necessary to read a corporation's private repository or source code to map out its logic flaws; you only need to analyze with precision how the infrastructure reacts when data inputs are pushed to extreme arithmetic and physical boundaries. ## What's next for Cyber Hunter Lab Our next objective is to deeply integrate the Qwen Cloud LLM APIs to generate dynamic, automated remediation guides in real time directly within our interface based on the raw telemetry data. We are also refining the mutation engine to support multi-step chaining, allowing the core to trace how a minor validation error in one endpoint can cascade into a full business logic bypass across the entire network.

Built With

• android
• javascript
• typescript