Quantstamp Presents Tech Talk at Facebook Headquarters on Blockchain Security

Decentralized applications (dApps) (e.g., DEXes) increasingly span multiple Ethereum-compatible chains, such as a number of L2s. Although these chains are intended to be compatible with the Ethereum Virtual Machine (EVM), subtle differences in opcode implementations can significantly alter smart contract behavior and security. This poses an important question: how can developers efficiently code and manage smart contracts targeting different chains?

The upcoming EVM hardfork, Pectra, amongst other changes, will implement EIP-7702, a proposal introducing a new transaction type that allows Externally Owned Accounts (EOAs) to delegate—and later undelegate—their behavior to smart contracts. While this upgrade enhances flexibility, it also disrupts long-standing security assumptions in many deployed contracts. With the risk that malicious actors may exploit these changes once Pectra is enabled, it is crucial to assess whether your codebase might be negatively impacted.

In recent months, AI agents have attracted significant attention by the promise of assisting users and automating complex processes across diverse applications. The rapid performance improvements of Large Language Models (LLMs) in natural language processing (NLP) tasks drive this trend. However, as the capabilities and reach of these agents expand, so do the risks. The rapid pace of development, combined with the intricacies of integrating LLMs into real-world infrastructures—especially in dynamic fields like blockchain—has created an urgent need to scrutinize them for security, compliance, and operational integrity.