VaradCodes31/BlockGuard

BlockGuard is currently at a prototype stage with zero community traction (0 stars, 0 forks). The core methodology—applying Long Short-Term Memory (LSTM) networks to Ethereum Virtual Machine (EVM) opcodes—is a well-documented academic approach found in several research papers and undergraduate projects over the last 3-4 years. While the inclusion of explainable AI (XAI) via gradient signals and a Streamlit dashboard adds some usability, it does not constitute a technical moat. The project faces extreme competition from established symbolic execution tools like Slither and Mythril, as well as modern LLM-based security auditors (e.g., Cyfrin Solodit, CertiK's Skyfall). Frontier labs (OpenAI/Anthropic) are already demonstrating superior zero-shot vulnerability detection capabilities through large context windows that understand contract semantics better than opcode-level LSTMs. Without a unique dataset of labeled vulnerabilities or a more advanced architecture like Graph Neural Networks (GNNs) for control-flow analysis, this project remains a personal experiment with high displacement risk.